Add files using upload-large-folder tool

Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/retrieval_plan.json

@@ -0,0 +1,354 @@
+{
+  "query": "You are running a bioagent-bench task with local files already prepared.\n\nTask ID: comparative-genomics\nTask name: Comparative Genomics: Co-evolving Gene Clusters\nBenchmark prompt:\nReconstruct phylogeny and identify COGs across four Micrococcus genomes; filter clusters present in all genomes, coding-only, with high-confidence annotations. The output should be a CSV file with the following columns: 'cluster_number, 'consensus_annotation'.<example>cluster_number,consensus_annotation\n1,K07222  K07222, putative flavoprotein involved in K+ transport\n2,K01069  gloB, gloC, HAGH, hydroxyacylglutathione hydrolase [EC:3.1.2.6]\n</example>\nData background:\nThe datasets consists FASTA sequences and GFF annotations of a microbial genome for Micrococcus. The goal of is to do phylogenetic reconstruction of clusters of orthologous co-evolving genes; identify functionally conserved gene clusters across the genomes and group them into co-evolving functional modules.\n\nConstraints:\n1. Use only the benchmark inputs and references explicitly listed below.\n2. Save the required final deliverables exactly to the paths listed below.\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\n5. Return a concise final summary after writing the required files.\n\nBenchmark data policy:\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/data\n- Allowed reference directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/reference\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/results\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than comparative-genomics>\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\n- Do not download external databases or install new packages during the benchmark run.\n\nInput data directory:\n/225040511/project/bioagent-bench/dataset/comparative-genomics/data\nVisible input files:\n- GCF_002008305.4_ASM200830v4_genomic.fna\n- GCF_003691675.1_ASM369167v1_genomic.fna\n- GCF_005280335.1_ASM528033v1_genomic.fna\n- GCF_020097155.1_ASM2009715v1_genomic.fna\n- GCF_023573625.1_ASM2357362v1_genomic.fna\n- assembly_data_report.jsonl\n- genomic.gff\n\nReference data directory:\n/225040511/project/bioagent-bench/dataset/comparative-genomics/reference\nVisible reference files:\n- Actinobacteria.RData\n\nRequired final output paths:\n- cluster_annotation_mapping.csv: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/cluster_annotation_mapping.csv",
+  "query_context": {},
+  "mcp_enabled": false,
+  "mcp_config": null,
+  "planning_context_text": "{\"prompt\": \"You are running a bioagent-bench task with local files already prepared.\\n\\nTask ID: comparative-genomics\\nTask name: Comparative Genomics: Co-evolving Gene Clusters\\nBenchmark prompt:\\nReconstruct phylogeny and identify COGs across four Micrococcus genomes; filter clusters present in all genomes, coding-only, with high-confidence annotations. The output should be a CSV file with the following columns: 'cluster_number, 'consensus_annotation'.<example>cluster_number,consensus_annotation\\n1,K07222  K07222, putative flavoprotein involved in K+ transport\\n2,K01069  gloB, gloC, HAGH, hydroxyacylglutathione hydrolase [EC:3.1.2.6]\\n</example>\\nData background:\\nThe datasets consists FASTA sequences and GFF annotations of a microbial genome for Micrococcus. The goal of is to do phylogenetic reconstruction of clusters of orthologous co-evolving genes; identify functionally conserved gene clusters across the genomes and group them into co-evolving functional modules.\\n\\nConstraints:\\n1. Use only the benchmark inputs and references explicitly listed below.\\n2. Save the required final deliverables exactly to the paths listed below.\\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209\\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\\n5. Return a concise final summary after writing the required files.\\n\\nBenchmark data policy:\\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/data\\n- Allowed reference directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/reference\\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209\\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/results\\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than comparative-genomics>\\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\\n- Do not download external databases or install new packages during the benchmark run.\\n\\nInput data directory:\\n/225040511/project/bioagent-bench/dataset/comparative-genomics/data\\nVisible input files:\\n- GCF_002008305.4_ASM200830v4_genomic.fna\\n- GCF_003691675.1_ASM369167v1_genomic.fna\\n- GCF_005280335.1_ASM528033v1_genomic.fna\\n- GCF_020097155.1_ASM2009715v1_genomic.fna\\n- GCF_023573625.1_ASM2357362v1_genomic.fna\\n- assembly_data_report.jsonl\\n- genomic.gff\\n\\nReference data directory:\\n/225040511/project/bioagent-bench/dataset/comparative-genomics/reference\\nVisible reference files:\\n- Actinobacteria.RData\\n\\nRequired final output paths:\\n- cluster_annotation_mapping.csv: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/cluster_annotation_mapping.csv\", \"selected_resources_names\": {\"tools\": [{\"description\": \"Perform phylogenetic analysis on a set of protein sequences. This function aligns sequences, constructs a phylogenetic tree, and visualizes evolutionary relationships.\", \"name\": \"analyze_protein_phylogeny\", \"optional_parameters\": [{\"default\": \"./\", \"description\": \"Directory to save output files\", \"name\": \"output_dir\", \"type\": \"str\"}, {\"default\": \"clustalw\", \"description\": \"Method for sequence alignment: \\\"clustalw\\\", \\\"muscle\\\", or \\\"pre-aligned\\\"\", \"name\": \"alignment_method\", \"type\": \"str\"}, {\"default\": \"fasttree\", \"description\": \"Method for tree construction: \\\"iqtree\\\" or fallback to neighbor-joining\", \"name\": \"tree_method\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Path to a FASTA file containing protein sequences or a string with FASTA-formatted sequences\", \"name\": \"fasta_sequences\", \"type\": \"str\"}], \"id\": 74}, {\"description\": \"Perform comparative genomics and haplotype analysis on multiple genome samples. Aligns genome samples to a reference, identifies variants, analyzes shared and unique genomic regions, and determines haplotype structure.\", \"name\": \"analyze_comparative_genomics_and_haplotypes\", \"optional_parameters\": [{\"default\": \"./output\", \"description\": \"Directory to store output files\", \"name\": \"output_dir\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Paths to FASTA files containing whole-genome sequences to be analyzed\", \"name\": \"sample_fasta_files\", \"type\": \"List[str]\"}, {\"default\": null, \"description\": \"Path to the reference genome FASTA file\", \"name\": \"reference_genome_path\", \"type\": \"str\"}], \"id\": 85}, {\"description\": \"Analyze overlaps between two or more sets of genomic regions.\", \"name\": \"analyze_genomic_region_overlap\", \"optional_parameters\": [{\"default\": \"overlap_analysis\", \"description\": \"Prefix for output files\", \"name\": \"output_prefix\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"List of genomic region sets. Each item can be either a string path to a BED file or a list of tuples/lists with format (chrom, start, end) or (chrom, start, end, name)\", \"name\": \"region_sets\", \"type\": \"list\"}], \"id\": 88}, {\"description\": \"Executes the provided Python command in the notebook environment and returns the output.\", \"name\": \"run_python_repl\", \"optional_parameters\": [], \"required_parameters\": [{\"default\": null, \"description\": \"Python command to execute in the notebook environment\", \"name\": \"command\", \"type\": \"str\"}], \"id\": 174, \"module\": \"biomni.tool.support_tools\"}, {\"description\": \"Query the UniProt REST API using either natural language or a direct endpoint.\", \"name\": \"query_uniprot\", \"optional_parameters\": [{\"default\": null, \"description\": \"Full or partial UniProt API endpoint URL to query directly (e.g., 'https://rest.uniprot.org/uniprotkb/P01308')\", \"name\": \"endpoint\", \"type\": \"str\"}, {\"default\": 5, \"description\": \"Maximum number of results to return\", \"name\": \"max_results\", \"type\": \"int\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Natural language query about proteins (e.g., \\\"Find information about human insulin\\\")\", \"name\": \"prompt\", \"type\": \"str\"}], \"id\": 177, \"module\": \"biomni.tool.database\"}, {\"description\": \"Take a natural language prompt and convert it to a structured KEGG API query.\", \"name\": \"query_kegg\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Direct KEGG endpoint to query\", \"default\": null}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about KEGG data\", \"default\": null}], \"id\": 182, \"module\": \"biomni.tool.database\"}, {\"description\": \"Identify a DNA or protein sequence using NCBI BLAST.\", \"name\": \"blast_sequence\", \"optional_parameters\": [], \"required_parameters\": [{\"name\": \"sequence\", \"type\": \"str\", \"description\": \"Query sequence\", \"default\": null}, {\"name\": \"database\", \"type\": \"str\", \"description\": \"BLAST database (e.g., core_nt or nr)\", \"default\": null}, {\"name\": \"program\", \"type\": \"str\", \"description\": \"BLAST program (blastn or blastp)\", \"default\": null}], \"id\": 199}], \"data_lake\": [], \"libraries\": [\"biopython\", \"scikit-bio\", \"pandas\", \"numpy\", \"scipy\", \"scikit-learn\", \"matplotlib\", \"seaborn\", \"mafft\", \"FastTree\", \"muscle\", \"iqtree2\"], \"know_how\": []}}",
+  "planning_latency_seconds": 1.5721461791545153,
+  "total_runtime_seconds": 3299.059731207788,
+  "selected_resources": {
+    "tools": [
+      {
+        "name": "analyze_protein_phylogeny",
+        "module": "biomni.tool.genetics",
+        "description": "Perform phylogenetic analysis on a set of protein sequences. This function aligns sequences, constructs a phylogenetic tree, and visualizes evolutionary relationships."
+      },
+      {
+        "name": "analyze_comparative_genomics_and_haplotypes",
+        "module": "biomni.tool.genomics",
+        "description": "Perform comparative genomics and haplotype analysis on multiple genome samples. Aligns genome samples to a reference, identifies variants, analyzes shared and unique genomic regions, and determines haplotype structure."
+      },
+      {
+        "name": "analyze_genomic_region_overlap",
+        "module": "biomni.tool.genomics",
+        "description": "Analyze overlaps between two or more sets of genomic regions."
+      },
+      {
+        "name": "run_python_repl",
+        "module": "biomni.tool.support_tools",
+        "description": "Executes the provided Python command in the notebook environment and returns the output."
+      },
+      {
+        "name": "query_uniprot",
+        "module": "biomni.tool.database",
+        "description": "Query the UniProt REST API using either natural language or a direct endpoint."
+      },
+      {
+        "name": "query_kegg",
+        "module": "biomni.tool.database",
+        "description": "Take a natural language prompt and convert it to a structured KEGG API query."
+      },
+      {
+        "name": "blast_sequence",
+        "module": "biomni.tool.database",
+        "description": "Identify a DNA or protein sequence using NCBI BLAST."
+      }
+    ],
+    "data_lake": [],
+    "libraries": [
+      {
+        "name": "biopython",
+        "description": "[Python Package] A set of tools for biological computation including parsers for bioinformatics files, access to online services, and interfaces to common bioinformatics programs."
+      },
+      {
+        "name": "scikit-bio",
+        "description": "[Python Package] Data structures, algorithms, and educational resources for bioinformatics, including sequence analysis, phylogenetics, and ordination methods."
+      },
+      {
+        "name": "pandas",
+        "description": "[Python Package] A fast, powerful, and flexible data analysis and manipulation library for Python."
+      },
+      {
+        "name": "numpy",
+        "description": "[Python Package] The fundamental package for scientific computing with Python, providing support for arrays, matrices, and mathematical functions."
+      },
+      {
+        "name": "scipy",
+        "description": "[Python Package] A Python library for scientific and technical computing, including modules for optimization, linear algebra, integration, and statistics."
+      },
+      {
+        "name": "scikit-learn",
+        "description": "[Python Package] A machine learning library featuring various classification, regression, and clustering algorithms."
+      },
+      {
+        "name": "matplotlib",
+        "description": "[Python Package] A comprehensive library for creating static, animated, and interactive visualizations in Python."
+      },
+      {
+        "name": "seaborn",
+        "description": "[Python Package] A statistical data visualization library based on matplotlib with a high-level interface for drawing attractive statistical graphics."
+      },
+      {
+        "name": "mafft",
+        "description": "[CLI Tool] A multiple sequence alignment program for unix-like operating systems. Use with subprocess.run(['mafft', ...])."
+      },
+      {
+        "name": "FastTree",
+        "description": "[CLI Tool] Phylogenetic trees from sequence alignments."
+      },
+      {
+        "name": "muscle",
+        "description": "[CLI Tool] Multiple sequence alignment tool."
+      },
+      {
+        "name": "iqtree2",
+        "description": "[CLI Tool] An efficient phylogenetic software for maximum likelihood analysis with built-in model selection and ultrafast bootstrap. Use with subprocess.run(['iqtree2', ...])."
+      }
+    ],
+    "know_how": []
+  },
+  "selected_resource_names": {
+    "tools": [
+      "analyze_protein_phylogeny",
+      "analyze_comparative_genomics_and_haplotypes",
+      "analyze_genomic_region_overlap",
+      "run_python_repl",
+      "query_uniprot",
+      "query_kegg",
+      "blast_sequence"
+    ],
+    "data_lake": [],
+    "libraries": [
+      "biopython",
+      "scikit-bio",
+      "pandas",
+      "numpy",
+      "scipy",
+      "scikit-learn",
+      "matplotlib",
+      "seaborn",
+      "mafft",
+      "FastTree",
+      "muscle",
+      "iqtree2"
+    ],
+    "know_how": []
+  },
+  "registered_tool_count": 224,
+  "registered_tool_names": [
+    "fetch_supplementary_info_from_doi",
+    "query_arxiv",
+    "query_scholar",
+    "query_pubmed",
+    "search_google",
+    "extract_url_content",
+    "extract_pdf_content",
+    "advanced_web_search_claude",
+    "analyze_circular_dichroism_spectra",
+    "analyze_rna_secondary_structure_features",
+    "analyze_protease_kinetics",
+    "analyze_enzyme_kinetics_assay",
+    "analyze_itc_binding_thermodynamics",
+    "analyze_protein_conservation",
+    "split_modalities",
+    "prepare_input_for_nnunet",
+    "segment_with_nn_unet",
+    "create_segmentation_visualization",
+    "quick_rigid_registration",
+    "quick_affine_registration",
+    "quick_deformable_registration",
+    "batch_register_images",
+    "calculate_similarity_metrics",
+    "create_registration_visualization",
+    "analyze_cell_migration_metrics",
+    "perform_crispr_cas9_genome_editing",
+    "analyze_calcium_imaging_data",
+    "analyze_in_vitro_drug_release_kinetics",
+    "analyze_myofiber_morphology",
+    "decode_behavior_from_neural_trajectories",
+    "simulate_whole_cell_ode_model",
+    "predict_protein_disorder_regions",
+    "analyze_cell_morphology_and_cytoskeleton",
+    "analyze_tissue_deformation_flow",
+    "find_n_glycosylation_motifs",
+    "predict_o_glycosylation_hotspots",
+    "list_glycoengineering_resources",
+    "analyze_ddr_network_in_cancer",
+    "analyze_cell_senescence_and_apoptosis",
+    "detect_and_annotate_somatic_mutations",
+    "detect_and_characterize_structural_variations",
+    "perform_gene_expression_nmf_analysis",
+    "analyze_copy_number_purity_ploidy_and_focal_events",
+    "quantify_cell_cycle_phases_from_microscopy",
+    "quantify_and_cluster_cell_motility",
+    "perform_facs_cell_sorting",
+    "analyze_flow_cytometry_immunophenotyping",
+    "analyze_mitochondrial_morphology_and_potential",
+    "annotate_open_reading_frames",
+    "annotate_plasmid",
+    "get_gene_coding_sequence",
+    "get_plasmid_sequence",
+    "align_sequences",
+    "pcr_simple",
+    "digest_sequence",
+    "find_restriction_sites",
+    "find_restriction_enzymes",
+    "find_sequence_mutations",
+    "design_knockout_sgrna",
+    "get_oligo_annealing_protocol",
+    "get_golden_gate_assembly_protocol",
+    "get_bacterial_transformation_protocol",
+    "design_primer",
+    "design_verification_primers",
+    "design_golden_gate_oligos",
+    "golden_gate_assembly",
+    "liftover_coordinates",
+    "bayesian_finemapping_with_deep_vi",
+    "analyze_cas9_mutation_outcomes",
+    "analyze_crispr_genome_editing",
+    "simulate_demographic_history",
+    "identify_transcription_factor_binding_sites",
+    "fit_genomic_prediction_model",
+    "perform_pcr_and_gel_electrophoresis",
+    "analyze_protein_phylogeny",
+    "annotate_celltype_scRNA",
+    "annotate_celltype_with_panhumanpy",
+    "create_scvi_embeddings_scRNA",
+    "create_harmony_embeddings_scRNA",
+    "get_uce_embeddings_scRNA",
+    "map_to_ima_interpret_scRNA",
+    "get_rna_seq_archs4",
+    "get_gene_set_enrichment_analysis_supported_database_list",
+    "gene_set_enrichment_analysis",
+    "analyze_chromatin_interactions",
+    "analyze_comparative_genomics_and_haplotypes",
+    "perform_chipseq_peak_calling_with_macs2",
+    "find_enriched_motifs_with_homer",
+    "analyze_genomic_region_overlap",
+    "unsupervised_celltype_transfer_between_scRNA_datasets",
+    "generate_embeddings_with_state",
+    "interspecies_gene_conversion",
+    "generate_gene_embeddings_with_ESM_models",
+    "generate_transcriptformer_embeddings",
+    "analyze_atac_seq_differential_accessibility",
+    "analyze_bacterial_growth_curve",
+    "isolate_purify_immune_cells",
+    "estimate_cell_cycle_phase_durations",
+    "track_immune_cells_under_flow",
+    "analyze_cfse_cell_proliferation",
+    "analyze_cytokine_production_in_cd4_tcells",
+    "analyze_ebv_antibody_titers",
+    "analyze_cns_lesion_histology",
+    "analyze_immunohistochemistry_image",
+    "optimize_anaerobic_digestion_process",
+    "analyze_arsenic_speciation_hplc_icpms",
+    "count_bacterial_colonies",
+    "annotate_bacterial_genome",
+    "enumerate_bacterial_cfu_by_serial_dilution",
+    "model_bacterial_growth_dynamics",
+    "quantify_biofilm_biomass_crystal_violet",
+    "segment_and_analyze_microbial_cells",
+    "segment_cells_with_deep_learning",
+    "simulate_generalized_lotka_volterra_dynamics",
+    "predict_rna_secondary_structure",
+    "simulate_microbial_population_dynamics",
+    "analyze_aortic_diameter_and_geometry",
+    "analyze_atp_luminescence_assay",
+    "analyze_thrombus_histology",
+    "analyze_intracellular_calcium_with_rhod2",
+    "quantify_corneal_nerve_fibers",
+    "segment_and_quantify_cells_in_multiplexed_images",
+    "analyze_bone_microct_morphometry",
+    "run_diffdock_with_smiles",
+    "docking_autodock_vina",
+    "run_autosite",
+    "retrieve_topk_repurposing_drugs_from_disease_txgnn",
+    "predict_admet_properties",
+    "predict_binding_affinity_protein_1d_sequence",
+    "analyze_accelerated_stability_of_pharmaceutical_formulations",
+    "run_3d_chondrogenic_aggregate_assay",
+    "grade_adverse_events_using_vcog_ctcae",
+    "analyze_radiolabeled_antibody_biodistribution",
+    "estimate_alpha_particle_radiotherapy_dosimetry",
+    "perform_mwas_cyp2c19_metabolizer_status",
+    "calculate_physicochemical_properties",
+    "analyze_xenograft_tumor_growth_inhibition",
+    "analyze_pixel_distribution",
+    "find_roi_from_image",
+    "analyze_western_blot",
+    "query_drug_interactions",
+    "check_drug_combination_safety",
+    "analyze_interaction_mechanisms",
+    "find_alternative_drugs_ddinter",
+    "query_fda_adverse_events",
+    "get_fda_drug_label_info",
+    "check_fda_drug_recalls",
+    "analyze_fda_safety_signals",
+    "reconstruct_3d_face_from_mri",
+    "analyze_abr_waveform_p1_metrics",
+    "analyze_ciliary_beat_frequency",
+    "analyze_protein_colocalization",
+    "perform_cosinor_analysis",
+    "calculate_brain_adc_map",
+    "analyze_endolysosomal_calcium_dynamics",
+    "analyze_fatty_acid_composition_by_gc",
+    "analyze_hemodynamic_data",
+    "simulate_thyroid_hormone_pharmacokinetics",
+    "quantify_amyloid_beta_plaques",
+    "engineer_bacterial_genome_for_therapeutic_delivery",
+    "analyze_bacterial_growth_rate",
+    "analyze_barcode_sequencing_data",
+    "analyze_bifurcation_diagram",
+    "create_biochemical_network_sbml_model",
+    "optimize_codons_for_heterologous_expression",
+    "simulate_gene_circuit_with_growth_feedback",
+    "identify_fas_functional_domains",
+    "perform_flux_balance_analysis",
+    "model_protein_dimerization_network",
+    "simulate_metabolic_network_perturbation",
+    "simulate_protein_signaling_network",
+    "compare_protein_structures",
+    "simulate_renin_angiotensin_system_dynamics",
+    "query_chatnt",
+    "run_python_repl",
+    "read_function_source_code",
+    "download_synapse_data",
+    "query_uniprot",
+    "query_alphafold",
+    "query_interpro",
+    "query_pdb",
+    "query_pdb_identifiers",
+    "query_kegg",
+    "query_stringdb",
+    "query_iucn",
+    "query_paleobiology",
+    "query_jaspar",
+    "query_worms",
+    "query_cbioportal",
+    "query_clinvar",
+    "query_geo",
+    "query_dbsnp",
+    "query_ucsc",
+    "query_ensembl",
+    "query_opentarget",
+    "query_monarch",
+    "query_openfda",
+    "query_gwas_catalog",
+    "query_gnomad",
+    "blast_sequence",
+    "query_reactome",
+    "query_regulomedb",
+    "query_pride",
+    "query_gtopdb",
+    "query_remap",
+    "query_mpd",
+    "query_emdb",
+    "query_synapse",
+    "query_pubchem",
+    "query_chembl",
+    "query_unichem",
+    "query_clinicaltrials",
+    "query_dailymed",
+    "query_quickgo",
+    "query_encode",
+    "region_to_ccre_screen",
+    "get_genes_near_ccre",
+    "test_pylabrobot_script",
+    "get_pylabrobot_documentation_liquid",
+    "get_pylabrobot_documentation_material",
+    "search_protocols",
+    "get_protocol_details",
+    "list_local_protocols",
+    "read_local_protocol"
+  ]
+}

Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/run_summary.json

@@ -0,0 +1,17 @@
+{
+  "task_id": "comparative-genomics",
+  "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209",
+  "outputs": [
+    {
+      "path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/cluster_annotation_mapping.csv",
+      "exists": true,
+      "size_bytes": 59535
+    }
+  ],
+  "planning_latency_seconds": 1.5721461791545153,
+  "total_runtime_seconds": 3299.059731207788,
+  "final_answer_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/final_answer.txt",
+  "metadata_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/run_metadata.json",
+  "retrieval_plan_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/retrieval_plan.json",
+  "output_validation_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/output_validation.json"
+}

Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/task_query.txt

@@ -0,0 +1,46 @@
+You are running a bioagent-bench task with local files already prepared.
+
+Task ID: comparative-genomics
+Task name: Comparative Genomics: Co-evolving Gene Clusters
+Benchmark prompt:
+Reconstruct phylogeny and identify COGs across four Micrococcus genomes; filter clusters present in all genomes, coding-only, with high-confidence annotations. The output should be a CSV file with the following columns: 'cluster_number, 'consensus_annotation'.<example>cluster_number,consensus_annotation
+1,K07222  K07222, putative flavoprotein involved in K+ transport
+2,K01069  gloB, gloC, HAGH, hydroxyacylglutathione hydrolase [EC:3.1.2.6]
+</example>
+Data background:
+The datasets consists FASTA sequences and GFF annotations of a microbial genome for Micrococcus. The goal of is to do phylogenetic reconstruction of clusters of orthologous co-evolving genes; identify functionally conserved gene clusters across the genomes and group them into co-evolving functional modules.
+
+Constraints:
+1. Use only the benchmark inputs and references explicitly listed below.
+2. Save the required final deliverables exactly to the paths listed below.
+3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209
+4. Keep final deliverables in the same schema/format requested by the benchmark prompt.
+5. Return a concise final summary after writing the required files.
+
+Benchmark data policy:
+- Allowed input data directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/data
+- Allowed reference directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/reference
+- Allowed scratch/output directory: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209
+- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/results
+- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than comparative-genomics>
+- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.
+- Do not download external databases or install new packages during the benchmark run.
+
+Input data directory:
+/225040511/project/bioagent-bench/dataset/comparative-genomics/data
+Visible input files:
+- GCF_002008305.4_ASM200830v4_genomic.fna
+- GCF_003691675.1_ASM369167v1_genomic.fna
+- GCF_005280335.1_ASM528033v1_genomic.fna
+- GCF_020097155.1_ASM2009715v1_genomic.fna
+- GCF_023573625.1_ASM2357362v1_genomic.fna
+- assembly_data_report.jsonl
+- genomic.gff
+
+Reference data directory:
+/225040511/project/bioagent-bench/dataset/comparative-genomics/reference
+Visible reference files:
+- Actinobacteria.RData
+
+Required final output paths:
+- cluster_annotation_mapping.csv: /225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/cluster_annotation_mapping.csv

Biomni/experiments/bioagent_bench/runs/no_mcp/eval_comp_evol_20260522_135929.json

@@ -0,0 +1,452 @@
+{
+  "evaluated_at_utc": "20260522_135931",
+  "judge_mode": "rule",
+  "primary_metric": "completion_rate",
+  "mean_completion_rate": 0.9166666666666667,
+  "results": [
+    {
+      "task_id": "comparative-genomics",
+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209",
+      "evaluated_at_utc": "20260522_135931",
+      "judge_mode": "rule",
+      "evaluation_results": {
+        "steps_completed": 5,
+        "steps_to_completion": 6,
+        "completion_rate": 0.8333333333333334,
+        "final_result_reached": true,
+        "results_match": 0.0,
+        "results_match_score": 0.0,
+        "results_match_pass": false,
+        "f1_score": null,
+        "notes": "Rule-mode approximation of the paper's LLM grader. Evidence-backed steps: inspect Micrococcus FASTA/GFF/reference inputs, predict or extract protein-coding genes, identify orthologous/co-evolving clusters across genomes, write cluster_number,consensus_annotation CSV, final artifact(s) exist; inferred most upstream steps completed."
+      },
+      "overall_score": 0.8333333333333334,
+      "score_definition": "BioAgent Bench-style completion rate: steps_completed / steps_to_completion. results_match is a numeric artifact/result matching score; results_match_pass and f1_score are reported separately.",
+      "rule_evaluation_results": {
+        "steps_completed": 5,
+        "steps_to_completion": 6,
+        "completion_rate": 0.8333333333333334,
+        "final_result_reached": true,
+        "results_match": 0.0,
+        "results_match_score": 0.0,
+        "results_match_pass": false,
+        "f1_score": null,
+        "notes": "Rule-mode approximation of the paper's LLM grader. Evidence-backed steps: inspect Micrococcus FASTA/GFF/reference inputs, predict or extract protein-coding genes, identify orthologous/co-evolving clusters across genomes, write cluster_number,consensus_annotation CSV, final artifact(s) exist; inferred most upstream steps completed."
+      },
+      "llm_evaluation_results": null,
+      "artifacts": [
+        {
+          "truth_file": "/225040511/project/bioagent-bench/dataset/comparative-genomics/results/cluster_annotation_mapping.csv",
+          "prediction_file": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/cluster_annotation_mapping.csv",
+          "prediction_exists": true,
+          "metrics": {
+            "pred_row_count": 1398,
+            "truth_row_count": 18,
+            "shared_key_count": 0,
+            "exact_key_precision": 0.0,
+            "exact_key_recall": 0.0,
+            "exact_key_f1": 0.0,
+            "key_precision": 0.0,
+            "key_recall": 0.0,
+            "key_f1": 0.0,
+            "match_strategy": "soft_row_similarity",
+            "match_precision": 0.0,
+            "match_recall": 0.0,
+            "match_f1": 0.0,
+            "soft_key_columns": [
+              "consensus_annotation"
+            ],
+            "match_threshold": 0.68,
+            "soft_match_count": 0,
+            "mean_match_score": 0.0,
+            "unmatched_prediction_examples": [
+              {
+                "consensus_annotation": "K02313  dnaA, chromosomal replication initiator protein DnaA"
+              },
+              {
+                "consensus_annotation": "K02337  dnaN, DNA polymerase III subunit beta"
+              },
+              {
+                "consensus_annotation": "K03629  recF, DNA replication/repair protein RecF"
+              },
+              {
+                "consensus_annotation": "DciA family protein"
+              },
+              {
+                "consensus_annotation": "K02470  gyrB, DNA topoisomerase (ATP-hydrolyzing) subunit B"
+              }
+            ],
+            "unmatched_truth_examples": [
+              {
+                "consensus_annotation": "K07222  K07222, putative flavoprotein involved in K+ transport"
+              },
+              {
+                "consensus_annotation": "K07493  K07493, putative transposase"
+              },
+              {
+                "consensus_annotation": "K07493  K07493, putative transposase"
+              },
+              {
+                "consensus_annotation": "K07497  K07497, putative transposase"
+              },
+              {
+                "consensus_annotation": "K13638  zntR, MerR family transcriptional regulator, Zn(II)-responsive regulator of zntA"
+              }
+            ]
+          }
+        }
+      ],
+      "result_summaries": [
+        {
+          "path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/cluster_annotation_mapping.csv",
+          "exists": true,
+          "type": "csv",
+          "row_count": 1398,
+          "columns": [
+            "cluster_number",
+            "consensus_annotation"
+          ],
+          "preview": "cluster_number,consensus_annotation\n1,\"K02313  dnaA, chromosomal replication initiator protein DnaA\"\n2,\"K02337  dnaN, DNA polymerase III subunit beta\"\n3,\"K03629  recF, DNA replication/repair protein RecF\"\n4,DciA family protein\n5,\"K02470  gyrB, DNA topoisomerase (ATP-hydrolyzing) subunit B\"\n6,\"K02469  gyrA, DNA gyrase subunit A\"\n8,queuosine precursor transporter\n10,peptidylprolyl isomerase\n11,rhomboid family intramembrane serine protease\n12,cell division protein CrgA\n13,aminodeoxychorismate/anthranilate synthase component II\n15,protein kinase\n17,FtsW/RodA/SpoVE family cell cycle protein\n21,CoA ester lyase\n22,Glu/Leu/Phe/Val dehydrogenase\n23,YceI family protein\n24,dienelactone hydrolase family protein\n26,nitrate reductase\n27,carbohydrate kinase\n28,malate dehydrogenase\n29,Cof-type HAD-IIB family hydrolase\n32,low specificity L-threonine aldolase\n33,glycerophosphodiester phosphodiesterase\n34,aldehyde dehydrogenase family protein\n35,\"K01835  pgm, phosphoglucomutase (alpha-D-glucose-1%2C6-bisphosphate-dependent)\"\n36,transcriptional repressor\n37,acyl-CoA hydrolase\n38,PIG-L family deacetylase\n39,amidase\n41,\"K04518  pheA, prephenate dehydratase\"\n42,sphingosine kinase\n43,IS481 family transposase\n44,\"K01882  serS, serine--tRNA ligase\"\n45,Cof-type HAD-IIB family hydrolase\n47,inorganic diphosphatase\n48,D-alanyl-D-alanine carboxypeptidase\n49,zinc-dependent metalloprotease\n50,\"tilS, tRNA lysidine(34) synthetase TilS\"\n51,\"hpt, hypoxanthine phosphoribosyltransferase\"\n52,\"K03798  ftsH, ATP-dependent zinc metalloprotease FtsH\"\n53,\"K09007  folE, GTP cyclohydrolase I FolE\"\n54,\"folP, dihydropteroate synthase\"\n55,\"folB, dihydroneopterin aldolase\"\n56,\"folK, 2-amino-4-hydroxy-6-hydroxymethyldihydropteridine diphosphokinase\"\n62,glycerophosphodiester phosphodiesterase\n64,phage holin family protein\n66,\"panC, pantoate--beta-alanine ligase\"\n67,DNA-3-methyladenine glycosylase\n68,SRPBCC family protein\n69,M13 family metallopeptidase\n70,MarR family transcriptional regulator\n71,MFS transporter\n72,D-glycerate dehydrogenase\n73,\"K04567  lysS, lysine--tRNA ligase\"\n75,Lsr2 family protein\n76,ATP-dependent Clp protease ATP-binding subunit\n77,Rv0909 family putative TA system antitoxin\n78,amino-acid N-acetyltransferase\n79,A/G-specific adenine glycosylase\n81,\"radA, DNA repair protein RadA\"\n82,FUSC family protein\n83,\"K02036  pstS, phosphate ABC transporter substrate-binding protein PstS\"\n84,\"K02037  pstC, phosphate ABC transporter permease subunit PstC\"\n85,\"K02038  pstA, phosphate ABC transporter permease PstA\"\n86,\"K02039  pstB, phosphate ABC transporter ATP-binding protein PstB\"\n87,inorganic phosphate transporter\n90,esterase\n93,glycerophosphodiester phosphodiesterase\n94,Nramp family divalent metal transporter\n95,thiamine-binding protein\n96,GNAT family N-acetyltransferase\n97,fused MFS/spermidine synthase\n98,universal stress protein\n99,metallopeptidase family protein\n100,cysteine hydrolase\n101,BCCT family transporter\n102,amino acid permease\n103,glycoside hydrolase family 13 protein\n105,exodeoxyribonuclease III\n... [truncated]"
+        }
+      ],
+      "truth_summaries": [
+        {
+          "path": "/225040511/project/bioagent-bench/dataset/comparative-genomics/results/cluster_annotation_mapping.csv",
+          "exists": true,
+          "type": "csv",
+          "row_count": 18,
+          "columns": [
+            "cluster_number",
+            "consensus_annotation"
+          ],
+          "preview": "\"cluster_number\",\"consensus_annotation\"\n1,\"K07222  K07222, putative flavoprotein involved in K+ transport\"\n1,\"K07493  K07493, putative transposase\"\n1,\"K07493  K07493, putative transposase\"\n1,\"K07497  K07497, putative transposase\"\n1,\"K13638  zntR, MerR family transcriptional regulator, Zn(II)-responsive regulator of zntA\"\n1,\"K16264  czcD, zitB, cobalt-zinc-cadmium efflux system protein\"\n1,\"K18230  tylC, oleB, carA, srmB, macrolide transport system ATP-binding/permease protein\"\n1,\"K21885  cmtR, ArsR family transcriptional regulator, cadmium/lead-responsive transcriptional repressor\"\n1,\"K21903  cadC, smtB, ArsR family transcriptional regulator, lead/cadmium/zinc/bismuth-responsive transcriptional repressor\"\n2,\"K01069  gloB, gloC, HAGH, hydroxyacylglutathione hydrolase [EC:3.1.2.6]\"\n2,\"K03325  ACR3, arsB, arsenite transporter\"\n2,\"K03892  arsR, ArsR family transcriptional regulator, arsenate/arsenite/antimonite-responsive transcriptional repressor\"\n2,\"K07090  K07090, uncharacterized protein\"\n2,\"K07485  K07485, transposase\"\n2,\"K07693  desR, two-component system, NarL family, response regulator DesR\"\n2,\"K16264  czcD, zitB, cobalt-zinc-cadmium efflux system protein\"\n2,\"K18701  arsC, arsenate-mycothiol transferase [EC:2.8.4.2]\"\n2,\"K21600  csoR, ricR, CsoR family transcriptional regulator, copper-sensing transcriptional repressor\""
+        }
+      ],
+      "trace_evidence": {
+        "processing_tree": [
+          "GCF_002008305.4_ASM200830v4.gff\t524892 bytes",
+          "GCF_002008305.4_ASM200830v4_proteins.faa\t1069070 bytes",
+          "GCF_003691675.1_ASM369167v1.gff\t526260 bytes",
+          "GCF_003691675.1_ASM369167v1_proteins.faa\t1060069 bytes",
+          "GCF_005280335.1_ASM528033v1.gff\t611157 bytes",
+          "GCF_005280335.1_ASM528033v1_proteins.faa\t1197752 bytes",
+          "GCF_020097155.1_ASM2009715v1.gff\t565685 bytes",
+          "GCF_020097155.1_ASM2009715v1_proteins.faa\t1128078 bytes",
+          "GCF_023573625.1_proteins.faa\t855595 bytes",
+          "agent_runtime/",
+          "agent_runtime/biomni_data/",
+          "agent_runtime/biomni_data/benchmark/",
+          "agent_runtime/biomni_data/data_lake/",
+          "all_genomes_db.pdb\t20480 bytes",
+          "all_genomes_db.phr\t2712183 bytes",
+          "all_genomes_db.pin\t94496 bytes",
+          "all_genomes_db.pjs\t627 bytes",
+          "all_genomes_db.pot\t141428 bytes",
+          "all_genomes_db.psq\t3900667 bytes",
+          "all_genomes_db.ptf\t16384 bytes",
+          "all_genomes_db.pto\t47144 bytes",
+          "all_genomes_proteins.faa\t5931526 bytes",
+          "all_vs_all_blast.txt\t13921052 bytes",
+          "annotated_cds_features.json\t426521 bytes",
+          "cluster_annotation_mapping.csv\t59535 bytes",
+          "execution_log.json\t161660 bytes",
+          "execution_log.txt\t153965 bytes",
+          "final_answer.txt\t2384 bytes",
+          "output_validation.json\t438 bytes",
+          "retrieval_plan.json\t23755 bytes",
+          "run_metadata.json\t4620 bytes",
+          "run_summary.json\t1076 bytes",
+          "task_query.txt\t2961 bytes"
+        ],
+        "path_mentions_from_trace": [
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/data",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/reference",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/results",
+          "/225040511/project/bioagent-bench/dataset/<any",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/reference/Actinobacteria.RData",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/data/biomni_data/runtime_mcp_configs/runtime_mcp_20260513_054017_942649.yaml",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/data/biomni_data/runtime_mcp_configs/runtime_mcp_20260513_053203_263845.yaml",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/data/GCF_002008305.4_ASM200830v4_genomic.fna",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/data/GCF_003691675.1_ASM369167v1_genomic.fna",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/data/GCF_005280335.1_ASM528033v1_genomic.fna",
+          "/225040511/project/bioagent-bench/dataset/comparative-genomics/data/GCF_020097155.1_ASM2009715v1_genomic.fna"
+        ]
+      },
+      "paper_alignment": {
+        "grader_inputs": [
+          "input data path",
+          "reference data path",
+          "expected outcome/truth as text summary",
+          "agent outcome as text summary",
+          "agent trace represented as folders/file paths",
+          "task prompt and grading logic"
+        ],
+        "grader_outputs": [
+          "steps_completed",
+          "steps_to_completion",
+          "final_result_reached",
+          "notes",
+          "results_match",
+          "results_match_score",
+          "results_match_pass",
+          "f1_score"
+        ]
+      }
+    },
+    {
+      "task_id": "evolution",
+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/evolution_20260521_133132",
+      "evaluated_at_utc": "20260522_135931",
+      "judge_mode": "rule",
+      "evaluation_results": {
+        "steps_completed": 7,
+        "steps_to_completion": 7,
+        "completion_rate": 1.0,
+        "final_result_reached": true,
+        "results_match": 0.0,
+        "results_match_score": 0.0,
+        "results_match_pass": true,
+        "f1_score": null,
+        "notes": "Rule-mode approximation of the paper's LLM grader. Evidence-backed steps: inspect ancestor/evolved-line reads, prepare or identify valid E. coli reference/assembly, align ancestor and evolved reads, call variants for all samples, identify variants shared by evolved lines and absent from ancestor, annotate variant/gene effects, write variants_shared.csv and gene_annotations.csv."
+      },
+      "overall_score": 1.0,
+      "score_definition": "BioAgent Bench-style completion rate: steps_completed / steps_to_completion. results_match is a numeric artifact/result matching score; results_match_pass and f1_score are reported separately.",
+      "rule_evaluation_results": {
+        "steps_completed": 7,
+        "steps_to_completion": 7,
+        "completion_rate": 1.0,
+        "final_result_reached": true,
+        "results_match": 0.0,
+        "results_match_score": 0.0,
+        "results_match_pass": true,
+        "f1_score": null,
+        "notes": "Rule-mode approximation of the paper's LLM grader. Evidence-backed steps: inspect ancestor/evolved-line reads, prepare or identify valid E. coli reference/assembly, align ancestor and evolved reads, call variants for all samples, identify variants shared by evolved lines and absent from ancestor, annotate variant/gene effects, write variants_shared.csv and gene_annotations.csv."
+      },
+      "llm_evaluation_results": null,
+      "artifacts": [
+        {
+          "truth_file": "/225040511/project/bioagent-bench/dataset/evolution/results/variants_shared.csv",
+          "prediction_file": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/evolution_20260521_133132/variants_shared.csv",
+          "prediction_exists": true,
+          "metrics": {
+            "pred_row_count": 17,
+            "truth_row_count": 16,
+            "shared_key_count": 0,
+            "exact_key_precision": 0.0,
+            "exact_key_recall": 0.0,
+            "exact_key_f1": 0.0,
+            "key_precision": 0.0,
+            "key_recall": 0.0,
+            "key_f1": 0.0,
+            "match_strategy": "exact_key",
+            "match_precision": 0.0,
+            "match_recall": 0.0,
+            "match_f1": 0.0
+          }
+        },
+        {
+          "truth_file": "/225040511/project/bioagent-bench/dataset/evolution/results/gene_annotations.csv",
+          "prediction_file": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/evolution_20260521_133132/gene_annotations.csv",
+          "prediction_exists": true,
+          "metrics": {
+            "pred_row_count": 10,
+            "truth_row_count": 16,
+            "shared_key_count": 0,
+            "exact_key_precision": 0.0,
+            "exact_key_recall": 0.0,
+            "exact_key_f1": 0.0,
+            "key_precision": 0.0,
+            "key_recall": 0.0,
+            "key_f1": 0.0,
+            "match_strategy": "exact_key",
+            "match_precision": 0.0,
+            "match_recall": 0.0,
+            "match_f1": 0.0
+          }
+        }
+      ],
+      "result_summaries": [
+        {
+          "path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/evolution_20260521_133132/variants_shared.csv",
+          "exists": true,
+          "type": "csv",
+          "row_count": 17,
+          "columns": [
+            "chrom",
+            "pos",
+            "ref",
+            "alt",
+            "gene",
+            "impact",
+            "effect",
+            "status"
+          ],
+          "preview": "chrom,pos,ref,alt,gene,impact,effect,status\nNC_000913.3,895039,A,C,potG,MODERATE,missense_variant,shared\nNC_000913.3,1269766,C,T,ldrA_2,MODERATE,missense_variant,shared\nNC_000913.3,1404973,A,C,tsaR_1,MODERATE,missense_variant,shared\nNC_000913.3,1415645,TCAC,T,recE,MODERATE,inframe_indel,shared\nNC_000913.3,1415649,TGCA,T,recE,MODERATE,inframe_indel,shared\nNC_000913.3,1639812,A,C,rrrD_2,MODERATE,missense_variant,shared\nNC_000913.3,1639857,CAT,C,rrrD_2,HIGH,frameshift_variant,shared\nNC_000913.3,1639862,T,TCC,rrrD_2,HIGH,frameshift_variant,shared\nNC_000913.3,1639872,G,A,rrrD_2,MODERATE,missense_variant,shared\nNC_000913.3,1639881,A,G,rrrD_2,MODERATE,missense_variant,shared\nNC_000913.3,1639893,G,T,rrrD_2,MODERATE,missense_variant,shared\nNC_000913.3,1639923,A,G,rrrD_2,MODERATE,missense_variant,shared\nNC_000913.3,1964254,A,C,flhA_2,MODERATE,missense_variant,shared\nNC_000913.3,3391514,T,G,tldD,MODERATE,missense_variant,shared\nNC_000913.3,3624745,G,A,unknown,MODERATE,missense_variant,shared\nNC_000913.3,3915728,T,G,atpC,MODERATE,missense_variant,shared\nNC_000913.3,4414665,T,G,aidB,MODERATE,missense_variant,shared"
+        },
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+          "/225040511/project/bioagent-bench/dataset/evolution/data/evol2_R2.fastq.gz"
+        ]
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+          "expected outcome/truth as text summary",
+          "agent outcome as text summary",
+          "agent trace represented as folders/file paths",
+          "task prompt and grading logic"
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+          "steps_to_completion",
+          "final_result_reached",
+          "notes",
+          "results_match",
+          "results_match_score",
+          "results_match_pass",
+          "f1_score"
+        ]
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+    }
+  ]
+}

Biomni/experiments/bioagent_bench/runs/no_mcp/latest_batch_summary.json

@@ -0,0 +1,180 @@
+{
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+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/alzheimer-mouse_20260521_095130",
+      "outputs": [
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+          "exists": true,
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+      "retrieval_plan_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/alzheimer-mouse_20260521_095130/retrieval_plan.json",
+      "output_validation_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/alzheimer-mouse_20260521_095130/output_validation.json"
+    },
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+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209",
+      "outputs": [
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+      "metadata_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/comparative-genomics_20260521_115209/run_metadata.json",
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+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/cystic-fibrosis_20260521_124708",
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+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/deseq_20260521_125049",
+      "outputs": [
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+      "task_id": "evolution",
+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/evolution_20260521_133132",
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+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/giab_20260521_155641",
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+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/metagenomics_20260521_183924",
+      "outputs": [
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+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/single-cell_20260521_184206",
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+      "total_runtime_seconds": 1097.9927689190954,
+      "final_answer_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/single-cell_20260521_184206/final_answer.txt",
+      "metadata_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/single-cell_20260521_184206/run_metadata.json",
+      "retrieval_plan_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/single-cell_20260521_184206/retrieval_plan.json",
+      "output_validation_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/single-cell_20260521_184206/output_validation.json"
+    },
+    {
+      "task_id": "transcript-quant",
+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/transcript-quant_20260521_190042",
+      "outputs": [
+        {
+          "path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/transcript-quant_20260521_190042/truth.tsv",
+          "exists": true,
+          "size_bytes": 5846
+        }
+      ],
+      "planning_latency_seconds": 3.0969363879412413,
+      "total_runtime_seconds": 48.948530750349164,
+      "final_answer_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/transcript-quant_20260521_190042/final_answer.txt",
+      "metadata_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/transcript-quant_20260521_190042/run_metadata.json",
+      "retrieval_plan_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/transcript-quant_20260521_190042/retrieval_plan.json",
+      "output_validation_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/transcript-quant_20260521_190042/output_validation.json"
+    },
+    {
+      "task_id": "viral-metagenomics",
+      "run_dir": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/viral-metagenomics_20260521_190131",
+      "outputs": [
+        {
+          "path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/viral-metagenomics_20260521_190131/taxonomy.csv",
+          "exists": true,
+          "size_bytes": 161
+        }
+      ],
+      "planning_latency_seconds": 2.6567907631397247,
+      "total_runtime_seconds": 323.6167436335236,
+      "final_answer_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/viral-metagenomics_20260521_190131/final_answer.txt",
+      "metadata_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/viral-metagenomics_20260521_190131/run_metadata.json",
+      "retrieval_plan_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/viral-metagenomics_20260521_190131/retrieval_plan.json",
+      "output_validation_path": "/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp/viral-metagenomics_20260521_190131/output_validation.json"
+    }
+  ]
+}

Biomni/experiments/bioagent_bench/runs/no_mcp/run_llm_eval_similarity_only.sh

@@ -0,0 +1,67 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+RUNS_ROOT="/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp"
+EVAL_PY="/225040511/project/Biomni/experiments/bioagent_bench/evaluate_bioagent_bench.py"
+OUT_ROOT="/225040511/project/Biomni/experiments/bioagent_bench/results"
+PYTHON_BIN="${PYTHON_BIN:-/225040511/miniconda3/envs/biomni_e1/bin/python}"
+
+if [[ -z "${DEEPSEEK_API_KEY:-}" ]]; then
+  echo "Missing DEEPSEEK_API_KEY" >&2
+  exit 2
+fi
+
+export DEEPSEEK_BASE_URL="${DEEPSEEK_BASE_URL:-https://api.deepseek.com/v1}"
+export DEEPSEEK_MODEL_NAME="${DEEPSEEK_MODEL_NAME:-deepseek-chat}"
+
+mkdir -p "${OUT_ROOT}"
+OUT="${OUT_ROOT}/no_mcp_llm_eval_selected_$(date -u +%Y%m%d_%H%M%S).json"
+
+"${PYTHON_BIN}" "${EVAL_PY}" \
+  --runs-root "${RUNS_ROOT}" \
+  --dataset-root /225040511/project/bioagent-bench/dataset \
+  --judge-mode llm \
+  --llm-provider deepseek \
+  --llm-model "${DEEPSEEK_MODEL_NAME}" \
+  --llm-base-url "${DEEPSEEK_BASE_URL}" \
+  --llm-api-key "${DEEPSEEK_API_KEY}" \
+  --task alzheimer-mouse \
+  --task comparative-genomics \
+  --task cystic-fibrosis \
+  --task deseq \
+  --task evolution \
+  --task giab \
+  --task metagenomics \
+  --task single-cell \
+  --task transcript-quant \
+  --task viral-metagenomics \
+  --output "${OUT}"
+
+export OUT
+"${PYTHON_BIN}" - <<'PY'
+import json
+import os
+from pathlib import Path
+
+p = Path(os.environ["OUT"])
+obj = json.loads(p.read_text())
+order = [
+    "alzheimer-mouse",
+    "comparative-genomics",
+    "cystic-fibrosis",
+    "deseq",
+    "evolution",
+    "giab",
+    "metagenomics",
+    "single-cell",
+    "transcript-quant",
+    "viral-metagenomics",
+]
+idx = {r["task_id"]: r for r in obj["results"]}
+
+print("source:", p)
+print("| " + " | ".join(order) + " |")
+print("|" + "|".join(["---:"] * len(order)) + "|")
+print("| " + " | ".join(f"{idx[t]['evaluation_results']['results_match']:.4f}" for t in order) + " |")
+PY
+

Biomni/experiments/bioagent_bench/runs/no_mcp/run_llm_eval_table.sh

@@ -0,0 +1,92 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT="/225040511/project/Biomni/experiments/bioagent_bench"
+RUNS_ROOT="/225040511/project/Biomni/experiments/bioagent_bench/runs/no_mcp"
+EVAL_PY="/225040511/project/Biomni/experiments/bioagent_bench/evaluate_bioagent_bench.py"
+SUMMARY_PY="/225040511/project/Biomni/experiments/bioagent_bench/scripts/summarize_biomni_task_metrics.py"
+GOLD_JSON="/225040511/project/Biomni/experiments/bioagent_bench/gold_tools.json"
+PYTHON_BIN="${PYTHON_BIN:-/225040511/miniconda3/envs/biomni_e1/bin/python}"
+
+if [[ -z "${DEEPSEEK_API_KEY:-}" ]]; then
+  echo "Missing DEEPSEEK_API_KEY" >&2
+  exit 2
+fi
+
+export DEEPSEEK_BASE_URL="${DEEPSEEK_BASE_URL:-https://api.deepseek.com/v1}"
+export DEEPSEEK_MODEL_NAME="${DEEPSEEK_MODEL_NAME:-deepseek-chat}"
+
+TS="$(date -u +%Y%m%d_%H%M%S)"
+OUT_DIR="${ROOT}/results/no_mcp_llm_eval_${TS}"
+mkdir -p "${OUT_DIR}"
+
+"${PYTHON_BIN}" "${EVAL_PY}" \
+  --all \
+  --runs-root "${RUNS_ROOT}" \
+  --dataset-root "/225040511/project/bioagent-bench/dataset" \
+  --judge-mode llm \
+  --llm-provider deepseek \
+  --llm-model "${DEEPSEEK_MODEL_NAME}" \
+  --llm-base-url "${DEEPSEEK_BASE_URL}" \
+  --llm-api-key "${DEEPSEEK_API_KEY}" \
+  --output "${OUT_DIR}/llm_eval.json"
+
+"${PYTHON_BIN}" "${SUMMARY_PY}" \
+  --runs-root "${RUNS_ROOT}" \
+  --evaluation-json "${OUT_DIR}/llm_eval.json" \
+  --gold "${GOLD_JSON}" \
+  --scale-label "no_mcp_llm" \
+  --out-json "${OUT_DIR}/task_metrics.json" \
+  --out-csv "${OUT_DIR}/task_metrics.csv"
+
+export OUT_DIR
+"${PYTHON_BIN}" - <<'PY'
+import csv
+import os
+from pathlib import Path
+
+out_dir = Path(os.environ["OUT_DIR"])
+task_csv = out_dir / "task_metrics.csv"
+rows = list(csv.DictReader(task_csv.open(encoding="utf-8")))
+
+def to_float(v):
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def mean_col(key):
+    vals = [to_float(r.get(key)) for r in rows]
+    vals = [x for x in vals if x is not None]
+    return round(sum(vals) / len(vals), 6) if vals else ""
+
+table_csv = out_dir / "table.csv"
+fieldnames = [
+    "Agent System",
+    "results_match",
+    "Selected Tools",
+    "Overhead/planning占整个流程",
+    "Gold Items",
+    "Context Tokens",
+    "Planning Latency",
+    "Selection Rate",
+]
+row = {
+    "Agent System": "Biomni no_mcp (LLM)",
+    "results_match": mean_col("results_match"),
+    "Selected Tools": mean_col("selected_tools"),
+    "Overhead/planning占整个流程": mean_col("overhead_planning_ratio"),
+    "Gold Items": mean_col("gold_items"),
+    "Context Tokens": mean_col("context_tokens"),
+    "Planning Latency": mean_col("planning_latency_seconds"),
+    "Selection Rate": mean_col("selection_rate"),
+}
+with table_csv.open("w", encoding="utf-8", newline="") as f:
+    w = csv.DictWriter(f, fieldnames=fieldnames)
+    w.writeheader()
+    w.writerow(row)
+
+print(f"out_dir={out_dir}")
+print(f"table_csv={table_csv}")
+print(row)
+PY

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/enrich_3xtg_kegg.csv

@@ -0,0 +1,286 @@
+Gene_set,Term,Overlap,P-value,Adjusted P-value,Old P-value,Old Adjusted P-value,Odds Ratio,Combined Score,Genes
+KEGG_2019_Mouse,Ascorbate and aldarate metabolism,10/27,2.576806942109413e-05,0.0073438997850118,0,0,6.768573428118899,71.51928134639657,UGT1A10;ALDH2;UGT1A1;UGT1A5;UGT1A2;UGT1A9;UGT1A6A;ALDH7A1;UGT1A7C;UGT1A6B
+KEGG_2019_Mouse,Cocaine addiction,10/48,0.0042339828646242,0.3333451779480207,0,0,3.0245855716310186,16.52818707363296,GRM2;GRIN2A;MAOB;PPP1R1B;FOSB;DRD1;DRD2;RGS9;GRIN2B;ADCY5
+KEGG_2019_Mouse,Porphyrin and chlorophyll metabolism,9/41,0.0045371087036117,0.3333451779480207,0,0,3.231558979974969,17.43576440349587,UGT1A10;ALAS2;UGT1A1;UGT1A5;UGT1A2;UGT1A9;UGT1A6A;UGT1A7C;UGT1A6B
+KEGG_2019_Mouse,Pentose and glucuronate interconversions,8/34,0.0046787407478858,0.3333451779480207,0,0,3.534324337326213,18.960682641149173,UGT1A10;UGT1A1;UGT1A5;UGT1A2;UGT1A9;UGT1A6A;UGT1A7C;UGT1A6B
+KEGG_2019_Mouse,Metabolism of xenobiotics by cytochrome P450,12/66,0.0058481610166319,0.3333451779480207,0,0,2.555067920585162,13.137208821889748,HSD11B1;UGT1A10;UGT1A1;UGT1A5;CYP2E1;UGT1A2;UGT1A9;UGT1A6A;UGT1A6B;UGT1A7C;GSTM6;CBR3
+KEGG_2019_Mouse,Retinol metabolism,14/91,0.0135768718957529,0.6329646520075534,0,0,2.0905096159333447,8.987910973661075,UGT1A10;UGT1A1;UGT1A6A;UGT1A6B;UGT1A7C;RPE65;CYP26B1;ALDH1A2;ALDH1A1;RDH16;UGT1A5;UGT1A2;UGT1A9;ALDH1A7
+KEGG_2019_Mouse,ErbB signaling pathway,13/84,0.0162253456128435,0.6329646520075534,0,0,2.1045999964656192,8.673436918991387,CDKN1A;SHC3;CAMK2A;PIK3CD;TGFA;CBL;EIF4EBP1;PAK6;ABL2;KRAS;PAK3;MAP2K7;HBEGF
+KEGG_2019_Mouse,Fanconi anemia pathway,9/51,0.0191701987843978,0.6329646520075534,0,0,2.460799213302342,9.73098035472379,RAD51C;EME2;RPA3;TOP3A;POLI;PMS2;BRCA1;MLH1;POLH
+KEGG_2019_Mouse,Drug metabolism,16/114,0.0199883574318174,0.6329646520075534,0,0,1.8773970933439368,7.345513823892213,UGT1A10;MAOB;UGT1A1;FMO2;UPB1;UGT1A6A;UGT1A7C;UGT1A6B;NME6;CES2H;UGT1A5;CYP2E1;UPP1;UGT1A2;UGT1A9;GSTM6
+KEGG_2019_Mouse,Oxytocin signaling pathway,20/154,0.0222975149144127,0.6354791750607641,0,0,1.717217316066172,6.531058352052632,GUCY1A2;GUCY1A1;PRKAB2;CDKN1A;PLA2G4D;NPR1;PLA2G4E;PLA2G4B;CAMK2A;KCNJ14;CACNA2D2;NFATC1;FOS;PTGS2;ACTB;RYR3;ADCY5;CAMK4;KRAS;CACNG3
+KEGG_2019_Mouse,Signaling pathways regulating pluripotency of stem cells,18/137,0.0262086348707655,0.6522966476107832,0,0,1.7395433944502912,6.334836639225476,FZD3;ZFHX3;DLX5;FZD6;PIK3CD;FZD10;HNF1A;IGF1;KLF4;ISL1;IGF1R;MEIS1;APC;ID1;ID4;KRAS;JAK3;BMPR1A
+KEGG_2019_Mouse,Osteoclast differentiation,17/128,0.0274651220046645,0.6522966476107832,0,0,1.7609722930477647,6.330410767573428,LILRA6;PIRA2;PIRA11;PIK3CD;NFATC1;FOS;PIRB;LILRB4A;OSCAR;CYLD;CTSK;CAMK4;FOSB;MAP2K7;FCGR2B;JUNB;IFNAR1
+KEGG_2019_Mouse,African trypanosomiasis,7/39,0.0338186194480654,0.6650842583975939,0,0,2.51029296875,8.501719039960486,LAMA4;HBB-B1;HBA-A2;HBB-BT;HBA-A1;ICAM1;IDO1
+KEGG_2019_Mouse,Non-small cell lung cancer,10/66,0.0370642671035465,0.6650842583975939,0,0,2.0503846497897844,6.756226405168473,CDKN1A;RASSF1;CDK6;PIK3CD;RARB;TGFA;KRAS;JAK3;FHIT;RXRG
+KEGG_2019_Mouse,Chemical carcinogenesis,13/94,0.0374241041791995,0.6650842583975939,0,0,1.8437659742553087,6.057583008650584,UGT1A10;UGT1A1;ARNT;PTGS2;UGT1A6A;UGT1A6B;UGT1A7C;HSD11B1;UGT1A5;CYP2E1;UGT1A2;UGT1A9;GSTM6
+KEGG_2019_Mouse,beta-Alanine metabolism,6/32,0.039617973066532,0.6650842583975939,0,0,2.647431893528084,8.547160794822346,ALDH2;GAD1;GADL1;GAD2;UPB1;ALDH7A1
+KEGG_2019_Mouse,Amphetamine addiction,10/68,0.0442634980743073,0.6650842583975939,0,0,1.979465808736208,6.171172535410261,GRIN2A;MAOB;CAMK4;CAMK2A;PPP1R1B;FOSB;FOS;DRD1;GRIN2B;ADCY5
+KEGG_2019_Mouse,Glycerophospholipid metabolism,13/97,0.0465021507523458,0.6650842583975939,0,0,1.7776259186234091,5.454212661076284,PLA2G4D;PLA2G4E;PLA2G4B;MBOAT7;CHAT;PLA2G6;SELENOI;GPD1;GPAT2;PNPLA6;LPIN1;DGKI;PLPP1
+KEGG_2019_Mouse,Arachidonic acid metabolism,12/89,0.0522725278133345,0.6650842583975939,0,0,1.7896185319382811,5.281673124144559,ALOX8;CYP4F18;PLA2G4D;PLA2G4E;GPX6;PLA2G4B;ALOX12B;CYP2E1;PTGDS;PLA2G6;PTGS2;CBR3
+KEGG_2019_Mouse,Taurine and hypotaurine metabolism,3/11,0.0524214176619201,0.6650842583975939,0,0,4.298238778054863,12.673099302482129,GAD1;GADL1;GAD2
+KEGG_2019_Mouse,cAMP signaling pathway,24/211,0.0530741618691869,0.6650842583975939,0,0,1.476057441870678,4.333800685998679,GLP1R;NPR1;HTR1D;CAMK2A;NPY1R;PIK3CD;NFATC1;FOS;SSTR2;GRIN2B;ADCY5;ADCYAP1;GRIN2A;HCAR1;PDE10A;ADORA2A;CAMK4;PPP1R1B;PDE3A;CNGA2;GHRL;DRD1;DRD2;VIP
+KEGG_2019_Mouse,Glycerolipid metabolism,9/61,0.0536734664671742,0.6650842583975939,0,0,1.986485510734572,5.810145341101644,DGAT2;ALDH2;LIPG;GPAT2;LPIN1;ALDH7A1;GLA;DGKI;PLPP1
+KEGG_2019_Mouse,Long-term depression,9/61,0.0536734664671742,0.6650842583975939,0,0,1.986485510734572,5.810145341101644,GUCY1A2;GUCY1A1;GRID2;PLA2G4D;PLA2G4E;PLA2G4B;KRAS;IGF1;IGF1R
+KEGG_2019_Mouse,Herpes simplex virus 1 infection,44/433,0.0633935586137539,0.7331814978773694,0,0,1.3029060520684794,3.5939269621552747,GM14322;ZFP984;ZFP605;H2-M5;ZFP109;ZFP868;H2-Q6;ZFP944;H2-Q7;PIK3CD;OAS1G;NXF3;ZFP182;ZFP52;EIF4EBP1;ZIM1;ZFP641;ZFP982;IKBKE;ZFP442;B2M;ZFP760;GM3055;ZFP286;ZFP39;ZFP930;CD74;ZFP951;ZFP950;POU2F1;ZFP933;GM12258;GM14391;EIF2AK2;2810021J22RIK;EIF2AK4;H2-AA;ZFP40;TRAF3;GM6710;ZFP772;ZFP871;IFNAR1;H2-AB1
+KEGG_2019_Mouse,Small cell lung cancer,12/92,0.064314166480471,0.7331814978773694,0,0,1.7222257053291536,4.725744888571506,CDKN1A;CDK6;LAMB3;TRAF3;CCNE1;LAMA4;PIK3CD;RARB;LAMC2;PTGS2;FHIT;RXRG
+KEGG_2019_Mouse,ECM-receptor interaction,11/83,0.0678415186991154,0.7436474165095347,0,0,1.7537855054302425,4.718701785825048,COL2A1;SV2C;LAMB3;LAMA4;ITGA10;SPP1;TNR;LAMC2;COL9A3;HSPG2;THBS1
+KEGG_2019_Mouse,Glioma,10/75,0.0766785186683879,0.7706258881425382,0,0,1.7656182264823468,4.534343632300459,CDKN1A;SHC3;CDK6;CAMK4;CAMK2A;PIK3CD;TGFA;KRAS;IGF1;IGF1R
+KEGG_2019_Mouse,Breast cancer,17/147,0.0817171955090722,0.7706258881425382,0,0,1.5020367682631834,3.7618373093060704,NOTCH2;FZD3;CDKN1A;SHC3;FZD6;PIK3CD;FZD10;IGF1;BRCA1;FOS;IGF1R;FGF7;CDK6;APC;KRAS;HES5;FGF10
+KEGG_2019_Mouse,Ovarian steroidogenesis,8/57,0.0842550143229243,0.7706258881425382,0,0,1.873007364296563,4.633646393575137,PLA2G4D;PLA2G4E;PLA2G4B;IGF1;PTGS2;CYP19A1;IGF1R;ADCY5
+KEGG_2019_Mouse,Tryptophan metabolism,7/48,0.0872931339819224,0.7706258881425382,0,0,1.9582926829268288,4.775264332383145,MAOB;ALDH2;CAT;ALDH7A1;INMT;DHTKD1;IDO1
+KEGG_2019_Mouse,Renal cell carcinoma,9/68,0.0933098766046341,0.7706258881425382,0,0,1.7501325809804629,4.151015766238054,ARNT2;CDKN1A;EGLN3;PIK3CD;ARNT;TGFA;PAK6;KRAS;PAK3
+KEGG_2019_Mouse,Mismatch repair,4/22,0.0951368730503653,0.7706258881425382,0,0,2.547307132459971,5.992383765427525,MSH2;RPA3;PMS2;MLH1
+KEGG_2019_Mouse,Malaria,7/49,0.0951540812919134,0.7706258881425382,0,0,1.9115625,4.496487787275823,GYPA;HBB-B1;HBA-A2;HBB-BT;HBA-A1;THBS1;ICAM1
+KEGG_2019_Mouse,Nicotine addiction,6/40,0.0980752511042904,0.7706258881425382,0,0,2.0236249402946687,4.6988980423366185,GABRB1;GRIN2A;SLC32A1;GABRA6;SLC17A8;GRIN2B
+KEGG_2019_Mouse,Choline metabolism in cancer,12/99,0.0990491470538802,0.7706258881425382,0,0,1.583050481029078,3.660232941599216,SLC5A7;SLC22A3;PLA2G4D;PLA2G4E;SLC22A2;PLA2G4B;EIF4EBP1;PIK3CD;KRAS;FOS;DGKI;PLPP1
+KEGG_2019_Mouse,Neuroactive ligand-receptor interaction,35/348,0.0993815972949187,0.7706258881425382,0,0,1.2860847580258354,2.969297468294543,GLP1R;GABRB1;FPR1;LPAR2;FPR3;ADM;FPR2;ADRA1A;GRM2;GRIN2A;GLRA2;CYSLTR2;NPW;GRM7;PENK;DRD1;DRD2;TAC1;GRID2;GABRA6;HTR1D;NPY1R;OPRK1;CCK;TACR1;ADRA2C;SSTR2;GRIN2B;SSTR3;MC3R;ADCYAP1;GAL;ADORA2A;GHRL;VIP
+KEGG_2019_Mouse,Steroid hormone biosynthesis,11/89,0.1000461679342944,0.7706258881425382,0,0,1.6183487565066512,3.725638736227792,HSD11B1;UGT1A10;UGT1A1;UGT1A5;CYP2E1;UGT1A2;UGT1A9;UGT1A6A;CYP19A1;UGT1A6B;UGT1A7C
+KEGG_2019_Mouse,Arginine and proline metabolism,7/50,0.1033935202216737,0.775451401662553,0,0,1.8670058139534884,4.236633837910859,AMD2;ALDH2;MAOB;P4HA3;ODC1;PRODH;ALDH7A1
+KEGG_2019_Mouse,Bladder cancer,6/41,0.107507972500132,0.785635183654811,0,0,1.9657000089229943,4.3838850363367365,CDKN1A;RASSF1;CDH1;KRAS;THBS1;HBEGF
+KEGG_2019_Mouse,Morphine addiction,11/92,0.1190402012709475,0.8481162610439374,0,0,1.558154645873944,3.3162112105528574,GABRB1;PDE10A;SLC32A1;PDE1C;GRK5;GABRA6;PDE1B;GNG7;PDE3A;DRD1;ADCY5
+KEGG_2019_Mouse,Melanoma,9/72,0.1220097077291278,0.8481162610439374,0,0,1.638655462184874,3.4471652087692317,CDKN1A;FGF7;CDK6;CDH1;PIK3CD;KRAS;IGF1;IGF1R;FGF10
+KEGG_2019_Mouse,Ras signaling pathway,24/233,0.1250845184583881,0.8487878038247768,0,0,1.3190870704585502,2.742072855150273,NTRK1;SHC3;ANGPT2;PLA2G4D;PLA2G4E;PLA2G4B;TGFA;PIK3CD;IGF1;PLA2G6;RASGRP2;GRIN2B;IGF1R;RASGRP3;RASSF1;GRIN2A;FGF7;GNG7;KDR;PAK6;ABL2;KRAS;PAK3;FGF10
+KEGG_2019_Mouse,Hematopoietic cell lineage,11/94,0.1327526860886501,0.8798724543084953,0,0,1.5204426729474287,3.07018030005543,GYPA;CD4;CD59A;ANPEP;IL3RA;CD7;FCER2A;CSF2RA;H2-AA;CD22;H2-AB1
+KEGG_2019_Mouse,alpha-Linolenic acid metabolism,4/25,0.1365485483216893,0.8844621879927607,0,0,2.1830496390696017,4.346615695963197,PLA2G4D;PLA2G4E;PLA2G4B;PLA2G6
+KEGG_2019_Mouse,Rap1 signaling pathway,21/209,0.1702491804293696,0.9073608888774668,0,0,1.2821798449196429,2.270089347611134,ANGPT2;FPR1;LPAR2;PIK3CD;IGF1;RASGRP2;GRIN2B;THBS1;ACTB;ADCY5;IGF1R;RASGRP3;GRIN2A;FGF7;ADORA2A;CDH1;ID1;KDR;KRAS;DRD2;FGF10
+KEGG_2019_Mouse,Circadian entrainment,11/99,0.1705414847316361,0.9073608888774668,0,0,1.4336622807017545,2.5358284372654176,GUCY1A2;ADCYAP1;PER1;GUCY1A1;GRIN2A;GNG7;CAMK2A;FOS;GRIN2B;RYR3;ADCY5
+KEGG_2019_Mouse,PI3K-Akt signaling pathway,34/357,0.1711679454240514,0.9073608888774668,0,0,1.2092214006089603,2.1344088680243507,CDKN1A;LAMA4;LPAR2;TGFA;PIK3CD;LAMC2;BRCA1;THBS1;IGF1R;FGF7;CCND2;GNG7;KDR;SPP1;EIF4EBP1;TNR;THEM4;JAK3;NTRK1;ANGPT2;LAMB3;IGF1;NR4A1;COL2A1;CDK6;CCNE1;ITGA10;IL3RA;COL9A3;SGK3;KRAS;SGK1;IFNAR1;FGF10
+KEGG_2019_Mouse,Thyroid cancer,5/37,0.1726316795134911,0.9073608888774668,0,0,1.7908278714107366,3.145759238618385,NTRK1;CDKN1A;CDH1;KRAS;RXRG
+KEGG_2019_Mouse,Ether lipid metabolism,6/47,0.1730124054826779,0.9073608888774668,0,0,1.6774881552688106,2.942971764576614,PLA2G4D;PLA2G4E;PLA2G4B;PLA2G6;SELENOI;PLPP1
+KEGG_2019_Mouse,FoxO signaling pathway,14/132,0.1741868028832917,0.9073608888774668,0,0,1.361092491514784,2.378681954802518,PRKAB2;CDKN1A;PLK2;FOXO6;PIK3CD;IGF1;SLC2A4;IGF1R;CCND2;CAT;HOMER3;SGK3;KRAS;SGK1
+KEGG_2019_Mouse,mTOR signaling pathway,16/154,0.1742269121339623,0.9073608888774668,0,0,1.3303090755062443,2.3245777372542755,FZD3;ATP6V1G2;MIOS;FZD6;PIK3CD;SLC3A2;FZD10;IGF1;IGF1R;SLC7A5;RRAGD;EIF4EBP1;SLC38A9;KRAS;SGK1;LPIN1
+KEGG_2019_Mouse,Measles,15/144,0.1811562861536059,0.9073608888774668,0,0,1.3339955591913053,2.278991557945501,TRP73;EIF2AK2;PIK3CD;EIF2AK4;FOS;CD209A;OAS1G;CCND2;CDK6;CCNE1;TRAF3;FCGR2B;JAK3;IKBKE;IFNAR1
+KEGG_2019_Mouse,VEGF signaling pathway,7/58,0.1819685781510032,0.9073608888774668,0,0,1.5734558823529412,2.6810449206958333,PLA2G4D;PLA2G4E;PLA2G4B;KDR;PIK3CD;KRAS;PTGS2
+KEGG_2019_Mouse,Antigen processing and presentation,10/90,0.1851053651477905,0.9073608888774668,0,0,1.4333907326236697,2.417886596573712,CD74;CD4;H2-M5;H2-Q6;H2-Q7;RFXANK;IFI30;B2M;H2-AA;H2-AB1
+KEGG_2019_Mouse,GnRH signaling pathway,10/90,0.1851053651477905,0.9073608888774668,0,0,1.4333907326236697,2.417886596573712,MAP3K2;EGR1;PLA2G4D;PLA2G4E;PLA2G4B;CAMK2A;KRAS;MAP2K7;HBEGF;ADCY5
+KEGG_2019_Mouse,Sphingolipid metabolism,6/48,0.1852580176452078,0.9073608888774668,0,0,1.637458731150174,2.76076481253801,SMPD3;CERS4;CERS5;GBA2;GLA;PLPP1
+KEGG_2019_Mouse,Pathways in cancer,49/535,0.1857644966210777,0.9073608888774668,0,0,1.1588178893484842,1.950609825499839,CDKN1A;PIK3CD;LAMC2;FZD10;IGF1R;FGF7;RASSF1;CCND2;CDH1;JAK3;APPL1;ARHGEF11;ARNT;FOS;MSH2;TRAF3;CCNE1;IL3RA;RARB;IFNAR1;NOTCH2;LAMA4;CAMK2A;LPAR2;TGFA;PTGS2;RASGRP2;CBL;CSF2RA;ADCY5;RASGRP3;GNG7;IL12RB1;RXRG;HES5;NTRK1;ARNT2;FZD3;EGLN3;LAMB3;FZD6;IGF1;MLH1;CXCL12;CDK6;APC;KRAS;GSTM6;FGF10
+KEGG_2019_Mouse,Pyruvate metabolism,5/38,0.1865763692800578,0.9073608888774668,0,0,1.7364657814096016,2.9153778169865863,ALDH2;GLO1;ACACB;ALDH7A1;ACACA
+KEGG_2019_Mouse,Type I diabetes mellitus,8/69,0.1878396226097212,0.9073608888774668,0,0,1.5035626774931052,2.514207518024208,H2-M5;H2-Q6;GAD1;H2-Q7;ICA1;GAD2;H2-AA;H2-AB1
+KEGG_2019_Mouse,p53 signaling pathway,8/71,0.2088998222718528,0.992274155791301,0,0,1.4556716995741386,2.2794369857203303,CDKN1A;CCND2;CDK6;CCNE1;ZMAT3;TRP73;IGF1;THBS1
+KEGG_2019_Mouse,B cell receptor signaling pathway,8/72,0.2197610871114105,0.999993572132612,0,0,1.432848655409631,2.1710727586504053,PIK3CD;NFATC1;KRAS;FOS;PIRB;FCGR2B;CD22;RASGRP3
+KEGG_2019_Mouse,Epstein-Barr virus infection,22/229,0.2199017291138168,0.999993572132612,0,0,1.2194394916106617,1.8469319805655144,CDKN1A;H2-M5;H2-Q6;H2-Q7;EIF2AK2;PIK3CD;H2-AA;ICAM1;OAS1G;CCND2;CDK6;CCNE1;TRAF3;FCER2A;VIM;CD247;MAP2K7;B2M;JAK3;IKBKE;IFNAR1;H2-AB1
+KEGG_2019_Mouse,Gastric cancer,15/150,0.2244114411114017,0.999993572132612,0,0,1.2742881072026802,1.9041357416585136,FZD3;CDKN1A;SHC3;FZD6;PIK3CD;FZD10;MLH1;FGF7;APC;CDH1;CCNE1;RARB;KRAS;RXRG;FGF10
+KEGG_2019_Mouse,Propanoate metabolism,4/31,0.235574619457597,0.999993572132612,0,0,1.697372981215776,2.4539388989970203,MCEE;DBT;ACACB;ACACA
+KEGG_2019_Mouse,Serotonergic synapse,13/132,0.2622580916191648,0.999993572132612,0,0,1.2523960650759676,1.676239678196516,GABRB1;MAOB;PLA2G4D;PLA2G4E;DUSP1;PLA2G4B;HTR1D;ALOX12B;PTGS2;ADCY5;ALOX8;GNG7;KRAS
+KEGG_2019_Mouse,Th1 and Th2 cell differentiation,9/87,0.2636472256620824,0.999993572132612,0,0,1.3224463271396938,1.7630105080686846,NOTCH2;CD4;NFATC1;FOS;CD247;IL12RB1;JAK3;H2-AA;H2-AB1
+KEGG_2019_Mouse,Renin secretion,8/76,0.2651197748479713,0.999993572132612,0,0,1.3482691387999852,1.7899264800521164,GUCY1A2;ADCYAP1;GUCY1A1;PDE1C;NPR1;PDE1B;PDE3A;ADCY5
+KEGG_2019_Mouse,Neurotrophin signaling pathway,12/121,0.2655787257833145,0.999993572132612,0,0,1.2620171982399129,1.6732378838612754,NTRK1;ZFP369;SHC3;KIDINS220;CAMK4;CAMK2A;ARHGDIG;TRP73;PIK3CD;KRAS;PSEN1;MAP2K7
+KEGG_2019_Mouse,Colorectal cancer,9/88,0.2745707989932691,0.999993572132612,0,0,1.3056352085676717,1.687593737930016,CDKN1A;MSH2;APC;PIK3CD;TGFA;KRAS;FOS;MLH1;APPL1
+KEGG_2019_Mouse,Synaptic vesicle cycle,8/77,0.2768716886554707,0.999993572132612,0,0,1.3286564972673138,1.7062621326071763,RIMS1;UNC13C;ATP6V1G2;SLC32A1;SLC6A13;SLC6A12;SLC17A8;SLC18A3
+KEGG_2019_Mouse,Glycosphingolipid biosynthesis,5/45,0.2931989731782813,0.999993572132612,0,0,1.432038077403246,1.756972974800964,B3GNT4;B3GNT3;FUT2;GLA;B4GALT4
+KEGG_2019_Mouse,GABAergic synapse,9/90,0.2967909740409467,0.999993572132612,0,0,1.2732582394659993,1.5466613894472734,GABRB1;SLC32A1;GABRA6;GNG7;GAD1;SLC6A13;SLC6A12;GAD2;ADCY5
+KEGG_2019_Mouse,Purine metabolism,13/136,0.2979161657636114,0.999993572132612,0,0,1.2114025155308017,1.4669395841273905,GUCY1A2;GUCY1A1;PDE1C;PDE6H;PDE1B;NPR1;PRUNE1;FHIT;ADCY5;PDE10A;NME6;PDE3A;PDE6A
+KEGG_2019_Mouse,Cholinergic synapse,11/113,0.2983537958952951,0.999993572132612,0,0,1.2359391124871002,1.4948377820574708,SLC5A7;GNG7;CAMK4;CHAT;CAMK2A;KCNJ14;PIK3CD;KRAS;FOS;SLC18A3;ADCY5
+KEGG_2019_Mouse,Hepatocellular carcinoma,16/171,0.2993138556710468,0.999993572132612,0,0,1.1833012307130837,1.4273719855450648,SMARCD1;FZD3;CDKN1A;SHC3;FZD6;TGFA;PIK3CD;FZD10;SMARCA2;ACTB;IGF1R;CDK6;APC;DPF3;KRAS;GSTM6
+KEGG_2019_Mouse,Human papillomavirus infection,32/360,0.3004200590292384,0.999993572132612,0,0,1.119008904374758,1.345690551932958,NOTCH2;CDKN1A;H2-M5;LAMA4;H2-Q6;H2-Q7;PIK3CD;LAMC2;FZD10;PSEN1;PTGS2;THBS1;CCND2;SPP1;EIF4EBP1;TNR;IKBKE;HES5;FZD3;ATP6V1G2;LAMB3;FZD6;EIF2AK2;COL2A1;CDK6;APC;CCNE1;TRAF3;ITGA10;COL9A3;KRAS;IFNAR1
+KEGG_2019_Mouse,Histidine metabolism,3/24,0.302933350799207,0.999993572132612,0,0,1.636266476665479,1.9540989056260911,MAOB;ALDH2;ALDH7A1
+KEGG_2019_Mouse,Longevity regulating pathway,10/102,0.3031968985210921,0.999993572132612,0,0,1.2456099752252865,1.48647713162652,PRKAB2;CAMK4;CAT;EIF4EBP1;PIK3CD;KRAS;IGF1;IGF1R;APPL1;ADCY5
+KEGG_2019_Mouse,Glutamatergic synapse,11/114,0.3083987838250949,0.999993572132612,0,0,1.2238727887680363,1.4397169284922111,GRM2;GRIN2A;GRM7;PLA2G4D;PLA2G4E;PLA2G4B;GNG7;HOMER3;SLC17A8;GRIN2B;ADCY5
+KEGG_2019_Mouse,Fatty acid biosynthesis,2/14,0.3118424343474316,0.999993572132612,0,0,1.908722741433022,2.224152987603514,ACACB;ACACA
+KEGG_2019_Mouse,Endometrial cancer,6/58,0.3219308144450548,0.999993572132612,0,0,1.3218421179070774,1.4982004671477342,CDKN1A;APC;CDH1;PIK3CD;KRAS;MLH1
+KEGG_2019_Mouse,Human T-cell leukemia virus 1 infection,22/245,0.3244871655018319,0.999993572132612,0,0,1.1309501916792803,1.2728949531799016,EGR1;EGR2;CDKN1A;H2-M5;H2-Q6;H2-Q7;BUB1B;PIK3CD;NFATC1;FOS;H2-AA;ICAM1;ADCY5;CD4;CCND2;PTTG1;CCNE1;KRAS;SLC25A31;B2M;JAK3;H2-AB1
+KEGG_2019_Mouse,HIF-1 signaling pathway,10/104,0.3245906502207875,0.999993572132612,0,0,1.218974406799984,1.3715783348971635,CDKN1A;EGLN3;ANGPT2;HKDC1;CAMK2A;EIF4EBP1;PIK3CD;ARNT;IGF1;IGF1R
+KEGG_2019_Mouse,Renin-angiotensin system,4/36,0.3275182298716635,0.999993572132612,0,0,1.431768558951965,1.5981566192175682,KLK1B22;ANPEP;KLK1B24;LNPEP
+KEGG_2019_Mouse,MAPK signaling pathway,26/294,0.333569440984493,0.999993572132612,0,0,1.1122046314914988,1.2210941544774498,TGFA;RASGRP2;IGF1R;RASGRP3;FGF7;KDR;MAP2K7;CACNG3;MAP3K2;NTRK1;ANGPT2;PLA2G4D;DUSP1;PLA2G4E;PLA2G4B;CACNA2D2;NFATC1;IGF1;FOS;DUSP6;NR4A1;TAOK1;MAPKAPK5;KRAS;PTPN7;FGF10
+KEGG_2019_Mouse,Vascular smooth muscle contraction,13/140,0.3348414973529823,0.999993572132612,0,0,1.172991236823126,1.2833673678140305,ARHGEF11;GUCY1A2;GUCY1A1;RAMP3;PLA2G4D;NPR1;PLA2G4E;PLA2G4B;ADM;PLA2G6;ADRA1A;ADCY5;ADORA2A
+KEGG_2019_Mouse,Focal adhesion,18/199,0.3350719274669622,0.999993572132612,0,0,1.1397974333209322,1.2462659814907244,SHC3;LAMB3;LAMA4;PIK3CD;LAMC2;IGF1;THBS1;ACTB;IGF1R;COL2A1;CCND2;ITGA10;KDR;SPP1;PAK6;TNR;COL9A3;PAK3
+KEGG_2019_Mouse,"Neomycin, kanamycin and gentamicin biosynthesis",1/5,0.3421994563021357,0.999993572132612,0,0,2.862546699875467,3.069684891092859,HKDC1
+KEGG_2019_Mouse,Aldosterone-regulated sodium reabsorption,4/38,0.3650824654365296,0.999993572132612,0,0,1.3474000954093428,1.3576834774814426,PIK3CD;KRAS;IGF1;SGK1
+KEGG_2019_Mouse,Phototransduction,3/27,0.3709019530340418,0.999993572132612,0,0,1.4314993765586037,1.4197861741921207,RCVRN;PDE6A;RGS9
+KEGG_2019_Mouse,Linoleic acid metabolism,5/50,0.3745972051618495,0.999993572132612,0,0,1.27257594673325,1.2495473485941646,PLA2G4D;PLA2G4E;PLA2G4B;CYP2E1;PLA2G6
+KEGG_2019_Mouse,Kaposi sarcoma-associated herpesvirus infection,19/216,0.3746892680168481,0.999993572132612,0,0,1.1051285657660883,1.084858535356505,CCR1;CDKN1A;ANGPT2;H2-M5;H2-Q6;H2-Q7;EIF2AK2;PIK3CD;NFATC1;FOS;PTGS2;ICAM1;CDK6;TRAF3;GNG7;KRAS;MAP2K7;IKBKE;IFNAR1
+KEGG_2019_Mouse,Gap junction,8/86,0.3871739838744547,0.999993572132612,0,0,1.1747726944725068,1.1147196256640146,MAP3K2;GUCY1A2;TUBB6;GUCY1A1;KRAS;DRD1;DRD2;ADCY5
+KEGG_2019_Mouse,Phosphonate and phosphinate metabolism,1/6,0.3950668633042352,0.999993572132612,0,0,2.2899128268991285,2.1266426244790506,SELENOI
+KEGG_2019_Mouse,Viral myocarditis,8/87,0.3996830194326157,0.999993572132612,0,0,1.1598388233152048,1.0636690445621368,H2-M5;H2-Q6;H2-Q7;ABL2;H2-AA;ACTB;ICAM1;H2-AB1
+KEGG_2019_Mouse,Homologous recombination,4/41,0.4211249882905299,0.999993572132612,0,0,1.2379491156783733,1.0706100930008249,RAD51C;RPA3;TOP3A;BRCA1
+KEGG_2019_Mouse,T cell receptor signaling pathway,9/101,0.4241683529902816,0.999993572132612,0,0,1.1203474451760354,0.9608378024667846,CD4;PIK3CD;PAK6;NFATC1;KRAS;FOS;CD247;MAP2K7;PAK3
+KEGG_2019_Mouse,Other glycan degradation,2/18,0.4304326951276698,0.999993572132612,0,0,1.4312305295950156,1.2064762536895306,AGA;GBA2
+KEGG_2019_Mouse,Pantothenate and CoA biosynthesis,2/18,0.4304326951276698,0.999993572132612,0,0,1.4312305295950156,1.2064762536895306,GADL1;UPB1
+KEGG_2019_Mouse,AMPK signaling pathway,11/126,0.4330898395186315,0.999993572132612,0,0,1.0954451345755694,0.9166795426770532,PRKAB2;SCD4;EIF4EBP1;PIK3CD;IGF1;SLC2A4;SCD1;ACACB;ADRA1A;ACACA;IGF1R
+KEGG_2019_Mouse,Th17 cell differentiation,9/102,0.4358752643314385,0.999993572132612,0,0,1.1082401388832814,0.9202816887718176,CD4;NFATC1;FOS;CD247;IL12RB1;JAK3;RXRG;H2-AA;H2-AB1
+KEGG_2019_Mouse,Circadian rhythm,3/30,0.4375399736184583,0.999993572132612,0,0,1.272236076475478,1.0516140677543373,PER1;PRKAB2;RORB
+KEGG_2019_Mouse,Salivary secretion,7/78,0.4383434877458818,0.999993572132612,0,0,1.1289964788732394,0.9311426202862748,GUCY1A2;BST1;GUCY1A1;LYZ2;ADRA1A;RYR3;ADCY5
+KEGG_2019_Mouse,Inflammatory mediator regulation of TRP channels,11/127,0.4435909747744124,0.999993572132612,0,0,1.0859422262552934,0.8827107113463969,NTRK1;PLA2G4D;PLA2G4E;TRPV4;PLA2G4B;CAMK2A;PIK3CD;IGF1;PLA2G6;ASIC1;ADCY5
+KEGG_2019_Mouse,TGF-beta signaling pathway,8/91,0.4496177784006165,0.999993572132612,0,0,1.1037018618564314,0.8822522931374532,AMHR2;SMURF2;ID1;CHRD;ID4;THBS1;BMP5;BMPR1A
+KEGG_2019_Mouse,Calcium signaling pathway,16/189,0.4517782270638859,0.999993572132612,0,0,1.059136835450856,0.8415518603419805,PDE1C;PDE1B;CAMK2A;TACR1;ADRA1A;RYR3;SLC8A3;GRIN2A;CYSLTR2;GNAL;PLCZ1;ADORA2A;CAMK4;ITPKA;DRD1;SLC25A31
+KEGG_2019_Mouse,Oocyte meiosis,10/116,0.4565694007862253,0.999993572132612,0,0,1.080268427830484,0.846946178820458,PTTG1;PLCZ1;CCNE1;CAMK2A;FBXO5;IGF1;PKMYT1;REC8;IGF1R;ADCY5
+KEGG_2019_Mouse,Intestinal immune network for IgA production,4/43,0.4578346242892255,0.999993572132612,0,0,1.1743365804501176,0.9174472151399712,CCL25;CXCL12;H2-AA;H2-AB1
+KEGG_2019_Mouse,Proteoglycans in cancer,17/203,0.4662464839581345,0.999993572132612,0,0,1.0465916007303713,0.7985921437461824,FZD3;CDKN1A;HPSE2;FZD6;CAMK2A;PIK3CD;FZD10;IGF1;CBL;HSPG2;THBS1;ACTB;IGF1R;KDR;KRAS;EZR;HBEGF
+KEGG_2019_Mouse,Fc epsilon RI signaling pathway,6/68,0.4682968362632194,0.999993572132612,0,0,1.1080373153875602,0.8406157432432985,PLA2G4D;PLA2G4E;PLA2G4B;PIK3CD;KRAS;MAP2K7
+KEGG_2019_Mouse,Regulation of lipolysis in adipocytes,5/56,0.4718856548660627,0.999993572132612,0,0,1.1224939413967834,0.8430138052362602,NPR1;NPY1R;PIK3CD;PTGS2;ADCY5
+KEGG_2019_Mouse,"Valine, leucine and isoleucine degradation",5/56,0.4718856548660627,0.999993572132612,0,0,1.1224939413967834,0.8430138052362602,MCEE;ALDH2;IVD;DBT;ALDH7A1
+KEGG_2019_Mouse,Hedgehog signaling pathway,4/44,0.475893886852151,0.999993572132612,0,0,1.144915782907049,0.8501690946831895,EVC2;CCND2;SMURF2;KIF3A
+KEGG_2019_Mouse,Biosynthesis of unsaturated fatty acids,3/32,0.4804525749214829,0.999993572132612,0,0,1.184366669533064,0.8681724563533666,HACD1;SCD4;SCD1
+KEGG_2019_Mouse,Galactose metabolism,3/32,0.4804525749214829,0.999993572132612,0,0,1.184366669533064,0.8681724563533666,GALT;HKDC1;GLA
+KEGG_2019_Mouse,Riboflavin metabolism,1/8,0.4884029948652162,0.999993572132612,0,0,1.6354741149261698,1.1720043093873822,ACP1
+KEGG_2019_Mouse,Staphylococcus aureus infection,8/95,0.4988285051927096,0.999993572132612,0,0,1.0527269198421427,0.732164118674706,C4B;FPR1;FPR3;FPR2;FCGR2B;H2-AA;ICAM1;H2-AB1
+KEGG_2019_Mouse,Glycosaminoglycan degradation,2/21,0.5119093009438632,0.999993572132612,0,0,1.2050500081980653,0.806910904436194,HPSE2;IDUA
+KEGG_2019_Mouse,Cushing syndrome,13/159,0.5159901944278639,0.999993572132612,0,0,1.0192803492549114,0.6744246975410539,FZD3;CDKN1A;KMT2A;FZD6;CAMK2A;ARNT;FZD10;ADCY5;NR4A1;CDK6;APC;CCNE1;KCNK2
+KEGG_2019_Mouse,Hippo signaling pathway,13/159,0.5159901944278639,0.999993572132612,0,0,1.0192803492549114,0.6744246975410539,FZD3;FZD6;TRP73;FZD10;ACTB;BMP5;RASSF1;CCND2;RASSF2;APC;CDH1;ID1;BMPR1A
+KEGG_2019_Mouse,Rheumatoid arthritis,7/84,0.5173852683965636,0.999993572132612,0,0,1.0406818181818185,0.6857754772946221,ATP6V1G2;CXCL12;CTSK;FOS;H2-AA;ICAM1;H2-AB1
+KEGG_2019_Mouse,Inflammatory bowel disease (IBD),5/59,0.5187821895916216,0.999993572132612,0,0,1.059959772506589,0.695621026422181,NFATC1;IL12RB1;H2-AA;IL18R1;H2-AB1
+KEGG_2019_Mouse,Lysine degradation,5/59,0.5187821895916216,0.999993572132612,0,0,1.059959772506589,0.695621026422181,SUV39H2;ALDH2;KMT2A;ALDH7A1;DHTKD1
+KEGG_2019_Mouse,Prostate cancer,8/97,0.5229494995930914,0.999993572132612,0,0,1.0289577053073902,0.6670428012599454,CDKN1A;CCNE1;INSRR;TGFA;PIK3CD;KRAS;IGF1;IGF1R
+KEGG_2019_Mouse,Prolactin signaling pathway,6/72,0.5249218982762516,0.999993572132612,0,0,1.040656407926864,0.6707090830622554,CCND2;GALT;SHC3;PIK3CD;KRAS;FOS
+KEGG_2019_Mouse,Dopaminergic synapse,11/135,0.5263895713357098,0.999993572132612,0,0,1.0154367774274395,0.6516197021801219,GRIN2A;GNAL;MAOB;GNG7;PPP1R1B;CAMK2A;DRD1;FOS;DRD2;GRIN2B;ADCY5
+KEGG_2019_Mouse,TNF signaling pathway,9/110,0.5278991810138431,0.999993572132612,0,0,1.0200126395618283,0.6516350325124471,TRAF3;PIK3CD;FOS;MAP2K7;TNFRSF1B;PTGS2;JUNB;IL18R1;ICAM1
+KEGG_2019_Mouse,Phospholipase D signaling pathway,12/149,0.5403048367439922,0.999993572132612,0,0,1.0025490240944557,0.6171910209783045,GRM2;SHC3;GRM7;PLA2G4D;PLA2G4E;PLA2G4B;LPAR2;PIK3CD;KRAS;DGKI;PLPP1;ADCY5
+KEGG_2019_Mouse,Base excision repair,3/35,0.5417158322959007,0.999993572132612,0,0,1.073156951371571,0.6578599238399014,TDG;APEX2;TDG-PS
+KEGG_2019_Mouse,Hypertrophic cardiomyopathy (HCM),7/86,0.5428646430069473,0.999993572132612,0,0,1.0142246835443038,0.6195850581955694,PRKAB2;TNNT2;ITGA10;CACNA2D2;IGF1;CACNG3;ACTB
+KEGG_2019_Mouse,ABC transporters,4/48,0.5455338329987252,0.999993572132612,0,0,1.04060568252708,0.6305971096112697,ABCA1;ABCC6;ABCA17;ABCC10
+KEGG_2019_Mouse,Toll-like receptor signaling pathway,8/99,0.5466476171858378,0.999993572132612,0,0,1.0062332914115275,0.6077154959713013,TRAF3;CTSK;SPP1;PIK3CD;FOS;MAP2K7;IKBKE;IFNAR1
+KEGG_2019_Mouse,Gastric acid secretion,6/74,0.5523098324317899,0.999993572132612,0,0,1.00993864129037,0.5995461351690913,CAMK2A;SSTR2;EZR;KCNK2;ACTB;ADCY5
+KEGG_2019_Mouse,Hepatitis B,13/163,0.5530424888717204,0.999993572132612,0,0,0.9918820577164368,0.5875120238063976,EGR2;CDKN1A;PIK3CD;NFATC1;FOS;HSPG2;CCNE1;TRAF3;KRAS;MAP2K7;JAK3;IKBKE;IFNAR1
+KEGG_2019_Mouse,Nicotinate and nicotinamide metabolism,3/36,0.561188329796114,0.999993572132612,0,0,1.040580367263659,0.6011419525301636,BST1;NMRK1;ASPDH
+KEGG_2019_Mouse,Primary immunodeficiency,3/36,0.561188329796114,0.999993572132612,0,0,1.040580367263659,0.6011419525301636,CD4;RFXANK;JAK3
+KEGG_2019_Mouse,Insulin signaling pathway,11/139,0.5663342238622817,0.999993572132612,0,0,0.9834889959273184,0.5591831975243987,PRKAB2;SHC3;EXOC7;HKDC1;EIF4EBP1;PIK3CD;KRAS;SLC2A4;CBL;ACACB;ACACA
+KEGG_2019_Mouse,Complement and coagulation cascades,7/88,0.5677682258476782,0.999993572132612,0,0,0.989074074074074,0.5598574636963151,C4B;SERPINA1A;SERPINA1B;SERPINA1C;SERPINA1D;CD59A;PROS1
+KEGG_2019_Mouse,Allograft rejection,5/63,0.5783456103378237,0.999993572132612,0,0,0.9866438503594644,0.5402700381114527,H2-M5;H2-Q6;H2-Q7;H2-AA;H2-AB1
+KEGG_2019_Mouse,Basal cell carcinoma,5/63,0.5783456103378237,0.999993572132612,0,0,0.9866438503594644,0.5402700381114527,CDKN1A;FZD3;APC;FZD6;FZD10
+KEGG_2019_Mouse,Fatty acid degradation,4/50,0.5784969488057942,0.999993572132612,0,0,0.9952534649705714,0.544724123605368,ACADL;ALDH2;ECI1;ALDH7A1
+KEGG_2019_Mouse,Chronic myeloid leukemia,6/76,0.5789527099483888,0.999993572132612,0,0,0.9809761756045328,0.5361373041599642,CDKN1A;SHC3;CDK6;PIK3CD;KRAS;CBL
+KEGG_2019_Mouse,"Alanine, aspartate and glutamate metabolism",3/37,0.5801497270933547,0.999993572132612,0,0,1.0099200528091536,0.549870220409955,FOLH1;GAD1;GAD2
+KEGG_2019_Mouse,Dilated cardiomyopathy (DCM),7/90,0.5920258286566439,0.999993572132612,0,0,0.9651355421686748,0.5059288919022549,TNNT2;ITGA10;CACNA2D2;IGF1;CACNG3;ACTB;ADCY5
+KEGG_2019_Mouse,Protein digestion and absorption,7/90,0.5920258286566439,0.999993572132612,0,0,0.9651355421686748,0.5059288919022549,SLC8A3;COL2A1;SLC7A8;COL5A1;COL11A1;SLC3A2;COL9A3
+KEGG_2019_Mouse,Central carbon metabolism in cancer,5/64,0.5926127817954563,0.999993572132612,0,0,0.9698681732580038,0.5074486792062259,NTRK1;SLC7A5;HKDC1;PIK3CD;KRAS
+KEGG_2019_Mouse,Graft-versus-host disease,5/64,0.5926127817954563,0.999993572132612,0,0,0.9698681732580038,0.5074486792062259,H2-M5;H2-Q6;H2-Q7;H2-AA;H2-AB1
+KEGG_2019_Mouse,IL-17 signaling pathway,7/91,0.6038931588326252,0.999993572132612,0,0,0.95359375,0.4809526232541901,TRAF3;LCN2;FOSB;FOS;PTGS2;IKBKE;S100A8
+KEGG_2019_Mouse,Asthma,2/25,0.6079036729199288,0.999993572132612,0,0,0.9952593796559664,0.4953792513835829,H2-AA;H2-AB1
+KEGG_2019_Mouse,Amyotrophic lateral sclerosis (ALS),4/52,0.6100494918834305,0.999993572132612,0,0,0.9536805988771054,0.4713234392038773,GRIN2A;CAT;TNFRSF1B;GRIN2B
+KEGG_2019_Mouse,Cell adhesion molecules (CAMs),13/170,0.6151796023388979,0.999993572132612,0,0,0.9472943921872627,0.4602344713595293,CNTNAP2;H2-M5;H2-Q6;H2-Q7;H2-AA;ICAM1;VCAN;CD4;CDH1;CDH15;NCAM2;CD22;H2-AB1
+KEGG_2019_Mouse,Transcriptional misregulation in cancer,14/183,0.615856656085984,0.999993572132612,0,0,0.9476964679050728,0.4593873749506463,NTRK1;ARNT2;CDKN1A;KMT2A;BCL11B;H3F3B;HPGD;LMO2;IGF1;DUSP6;IGF1R;MEIS1;CCND2;RXRG
+KEGG_2019_Mouse,Cellular senescence,14/185,0.6322367281320294,0.999993572132612,0,0,0.9365095098071607,0.4293815423481598,CDKN1A;H2-M5;H2-Q6;H2-Q7;PIK3CD;NFATC1;HIPK2;CCND2;CDK6;CCNE1;TRPV4;EIF4EBP1;KRAS;SLC25A31
+KEGG_2019_Mouse,Long-term potentiation,5/67,0.6337491400005448,0.999993572132612,0,0,0.9227880471990656,0.420885548722115,GRIN2A;CAMK4;CAMK2A;KRAS;GRIN2B
+KEGG_2019_Mouse,Fat digestion and absorption,3/40,0.6338254649526908,0.999993572132612,0,0,0.927882995214666,0.4230976231709694,ABCA1;DGAT2;PLPP1
+KEGG_2019_Mouse,Ferroptosis,3/40,0.6338254649526908,0.999993572132612,0,0,0.927882995214666,0.4230976231709694,SLC40A1;SLC3A2;SLC7A11
+KEGG_2019_Mouse,"Glycine, serine and threonine metabolism",3/40,0.6338254649526908,0.999993572132612,0,0,0.927882995214666,0.4230976231709694,ALAS2;MAOB;ALDH7A1
+KEGG_2019_Mouse,"Parathyroid hormone synthesis, secretion and action",8/107,0.6360014101489915,0.999993572132612,0,0,0.924517217200144,0.4183944255190577,ARHGEF11;EGR1;CDKN1A;MMP17;FOS;RXRG;HBEGF;ADCY5
+KEGG_2019_Mouse,Alcoholism,15/199,0.6402594125237673,0.999993572132612,0,0,0.9324304948656326,0.4157538365505722,SHC3;MAOB;H3F3B;GRIN2B;ADCY5;GRIN2A;ADORA2A;CAMK4;GNG7;PPP1R1B;FOSB;KRAS;DRD1;SLC29A1;DRD2
+KEGG_2019_Mouse,Human immunodeficiency virus 1 infection,18/238,0.6409389686477108,0.999993572132612,0,0,0.9357343097431204,0.4162343081803608,TRIM12C;APOBEC3;TRIM30D;H2-M5;H2-Q6;H2-Q7;PIK3CD;NFATC1;FOS;TNFRSF1B;CD4;GNG7;PAK6;KRAS;CD247;PAK3;MAP2K7;B2M
+KEGG_2019_Mouse,Butanoate metabolism,2/27,0.6502277791584966,0.999993572132612,0,0,0.9155389408099688,0.3940777590896375,GAD1;GAD2
+KEGG_2019_Mouse,Collecting duct acid secretion,2/27,0.6502277791584966,0.999993572132612,0,0,0.9155389408099688,0.3940777590896375,ATP6V1G2;SLC4A1
+KEGG_2019_Mouse,Maturity onset diabetes of the young,2/27,0.6502277791584966,0.999993572132612,0,0,0.9155389408099688,0.3940777590896375,HNF1B;HNF1A
+KEGG_2019_Mouse,Non-homologous end-joining,1/13,0.6635298947489607,0.999993572132612,0,0,0.9537671232876712,0.3912175053719425,PRKDC
+KEGG_2019_Mouse,Mucin type O-glycan biosynthesis,2/28,0.6699877329115731,0.999993572132612,0,0,0.8802779774742392,0.3525476995817224,GALNT11;GALNT13
+KEGG_2019_Mouse,RNA polymerase,2/28,0.6699877329115731,0.999993572132612,0,0,0.8802779774742392,0.3525476995817224,POLR1B;POLR3F
+KEGG_2019_Mouse,Lysosome,9/124,0.6731089415209385,0.999993572132612,0,0,0.8951515481308157,0.3543440287653854,CTSO;IDUA;CTSK;AP4S1;AP3S1;AGA;CTSE;MCOLN1;GLA
+KEGG_2019_Mouse,Apelin signaling pathway,10/138,0.6791529890475677,0.999993572132612,0,0,0.8935210551033187,0.3457112142132077,SLC8A3;EGR1;PRKAB2;CDH1;GNG7;CAMK4;SPP1;KRAS;RYR3;ADCY5
+KEGG_2019_Mouse,Ubiquitin mediated proteolysis,10/138,0.6791529890475677,0.999993572132612,0,0,0.8935210551033187,0.3457112142132077,PRKN;HERC2;SMURF2;UBA6;HUWE1;UBE2E2;KLHL13;BRCA1;CBL;MID1
+KEGG_2019_Mouse,Platelet activation,9/125,0.6823966548485542,0.999993572132612,0,0,0.8873861723706357,0.3391094654928229,GUCY1A2;GUCY1A1;PLA2G4D;PLA2G4E;PLA2G4B;PIK3CD;RASGRP2;ACTB;ADCY5
+KEGG_2019_Mouse,Vasopressin-regulated water reabsorption,3/43,0.6825284563400928,0.999993572132612,0,0,0.8581514962593516,0.3277718723676444,DYNC1I2;DCTN4;ARHGDIG
+KEGG_2019_Mouse,Adipocytokine signaling pathway,5/71,0.6844526044189626,0.999993572132612,0,0,0.8666723413914426,0.3285865789367697,PRKAB2;SLC2A4;TNFRSF1B;ACACB;RXRG
+KEGG_2019_Mouse,C-type lectin receptor signaling pathway,8/112,0.6865118733901385,0.999993572132612,0,0,0.8798287391157935,0.3309315307176456,CYLD;EGR2;PIK3CD;NFATC1;KRAS;PTGS2;IKBKE;CD209A
+KEGG_2019_Mouse,Fatty acid elongation,2/29,0.6888327625189503,0.999993572132612,0,0,0.8476289373485635,0.3159594183747625,HACD1;THEM4
+KEGG_2019_Mouse,Axon guidance,13/180,0.6960569768937515,0.999993572132612,0,0,0.8900818187965349,0.3224977900881588,FZD3;TRPC5;SEMA3C;CAMK2A;PIK3CD;UNC5D;SSH2;ABLIM1;CXCL12;PAK6;KRAS;SRGAP2;PAK3
+KEGG_2019_Mouse,Adherens junction,5/72,0.696357870082661,0.999993572132612,0,0,0.8536903497493804,0.3089433406575816,PTPRB;CDH1;ACP1;ACTB;IGF1R
+KEGG_2019_Mouse,Arrhythmogenic right ventricular cardiomyopathy (ARVC),5/72,0.696357870082661,0.999993572132612,0,0,0.8536903497493804,0.3089433406575816,ACTN2;ITGA10;CACNA2D2;CACNG3;ACTB
+KEGG_2019_Mouse,Influenza A,12/168,0.7062232389103857,0.999993572132612,0,0,0.8795273691825416,0.305920630072966,NXF3;EIF2AK2;PIK3CD;EIF2AK4;MAP2K7;IKBKE;H2-AA;ACTB;ICAM1;IFNAR1;H2-AB1;OAS1G
+KEGG_2019_Mouse,Thyroid hormone synthesis,5/73,0.707950090080616,0.999993572132612,0,0,0.8410901813909084,0.2904971415706111,IYD;GPX6;ASGR1;ADCY5;SLC5A5
+KEGG_2019_Mouse,Fc gamma R-mediated phagocytosis,6/87,0.7093139681000211,0.999993572132612,0,0,0.8472482476230134,0.2909933567802665,PLA2G4E;BIN1;PIK3CD;PLA2G6;FCGR2B;PLPP1
+KEGG_2019_Mouse,Thyroid hormone signaling pathway,8/115,0.7146107328849461,0.999993572132612,0,0,0.8550203690390606,0.2873016478841281,NOTCH2;PLCZ1;TBC1D4;PIK3CD;KRAS;RXRG;ACTB;MED13L
+KEGG_2019_Mouse,Thiamine metabolism,1/15,0.7154617326369072,0.999993572132612,0,0,0.8174257249599716,0.2736963379249685,ACP1
+KEGG_2019_Mouse,Bacterial invasion of epithelial cells,5/74,0.719228358283716,0.999993572132612,0,0,0.8288552353036964,0.2731710966280697,SHC3;CDH1;PIK3CD;CBL;ACTB
+KEGG_2019_Mouse,Aldosterone synthesis and secretion,7/102,0.7212799131382995,0.999993572132612,0,0,0.8426710526315789,0.2753242175704266,NR4A1;KCNK9;NPR1;CAMK4;CAMK2A;DAGLB;ADCY5
+KEGG_2019_Mouse,Fluid shear stress and atherosclerosis,10/143,0.7214002512058846,0.999993572132612,0,0,0.8596946342060536,0.2807428789632051,DUSP1;TRPV4;KDR;PIK3CD;FOS;MAP2K7;ACTB;GSTM6;ICAM1;BMPR1A
+KEGG_2019_Mouse,Chemokine signaling pathway,14/197,0.7225164376360633,0.999993572132612,0,0,0.8745227583793852,0.2842331089388921,CCR1;CCL25;SHC3;PIK3CD;RASGRP2;ADCY5;CXCL12;GRK5;GNG7;CCL27A;CCL27B;KRAS;CCR6;JAK3
+KEGG_2019_Mouse,Glyoxylate and dicarboxylate metabolism,2/31,0.7238582853421236,0.999993572132612,0,0,0.7890858309163176,0.2550006963885549,MCEE;CAT
+KEGG_2019_Mouse,Pancreatic cancer,5/75,0.7301925400283755,0.999993572132612,0,0,0.8169698591046906,0.256893742652107,CDKN1A;CDK6;PIK3CD;TGFA;KRAS
+KEGG_2019_Mouse,Primary bile acid biosynthesis,1/16,0.738341432192837,0.999993572132612,0,0,0.7628891656288916,0.2314216015141566,CH25H
+KEGG_2019_Mouse,Progesterone-mediated oocyte maturation,6/90,0.7396077523861038,0.999993572132612,0,0,0.8168555367181226,0.2463924629638369,PIK3CD;KRAS;IGF1;PKMYT1;IGF1R;ADCY5
+KEGG_2019_Mouse,Wnt signaling pathway,11/160,0.7473917296998626,0.999993572132612,0,0,0.8439050646751106,0.2457163164191902,FZD3;CCND2;ZNRF3;GM9839;APC;FZD6;CAMK2A;NFATC1;FZD10;PSEN1;LGR5
+KEGG_2019_Mouse,Type II diabetes mellitus,3/48,0.7526559317000716,0.999993572132612,0,0,0.7625935162094764,0.2166887251434464,HKDC1;PIK3CD;SLC2A4
+KEGG_2019_Mouse,Estrogen signaling pathway,9/134,0.7584752899915241,0.999993572132612,0,0,0.8230888610763454,0.2275388480965204,SHC3;TGFA;PIK3CD;KRT12;KRAS;FOS;KRT20;HBEGF;ADCY5
+KEGG_2019_Mouse,Selenocompound metabolism,1/17,0.759382417718748,0.999993572132612,0,0,0.7151696762141968,0.196850299124179,INMT
+KEGG_2019_Mouse,Autoimmune thyroid disease,5/78,0.7612097177361434,0.999993572132612,0,0,0.7832674909787424,0.2137116973736069,H2-M5;H2-Q6;H2-Q7;H2-AA;H2-AB1
+KEGG_2019_Mouse,Amino sugar and nucleotide sugar metabolism,3/49,0.7650779340735688,0.999993572132612,0,0,0.7459747370703675,0.1997553066548636,GALT;HKDC1;GMPPA
+KEGG_2019_Mouse,Notch signaling pathway,3/49,0.7650779340735688,0.999993572132612,0,0,0.7459747370703675,0.1997553066548636,NOTCH2;PSEN1;HES5
+KEGG_2019_Mouse,Glutathione metabolism,4/64,0.7666693948961671,0.999993572132612,0,0,0.7624454148471616,0.2025814472204115,ANPEP;GPX6;ODC1;GSTM6
+KEGG_2019_Mouse,Prion diseases,2/34,0.7700979682443653,0.999993572132612,0,0,0.7149922118380062,0.1867828070698562,EGR1;NCAM2
+KEGG_2019_Mouse,Retrograde endocannabinoid signaling,10/150,0.7741592389330694,0.999993572132612,0,0,0.8163968154575544,0.2089793721665875,RIMS1;GABRB1;SLC32A1;GABRA6;GNG7;NDUFA3;SLC17A8;DAGLB;PTGS2;ADCY5
+KEGG_2019_Mouse,Cell cycle,8/123,0.781109469317725,0.999993572132612,0,0,0.7951926475786497,0.1964443704938208,CDKN1A;CCND2;CDK6;PTTG1;CCNE1;PRKDC;BUB1B;PKMYT1
+KEGG_2019_Mouse,Fructose and mannose metabolism,2/35,0.7839392888937357,0.999993572132612,0,0,0.6932880203908242,0.1687627345801416,HKDC1;GMPPA
+KEGG_2019_Mouse,Phagosome,12/180,0.7892466702042832,0.999993572132612,0,0,0.816166592028661,0.1931673467161626,DYNC1I2;TUBB6;ATP6V1G2;H2-M5;H2-Q6;H2-Q7;FCGR2B;THBS1;H2-AA;CD209A;ACTB;H2-AB1
+KEGG_2019_Mouse,Arginine biosynthesis,1/19,0.7965271215477802,0.999993572132612,0,0,0.635637193856372,0.1446037109006728,OTC
+KEGG_2019_Mouse,Leishmaniasis,4/67,0.7970322741315354,0.999993572132612,0,0,0.7260196655081247,0.1647048986331403,FOS;PTGS2;H2-AA;H2-AB1
+KEGG_2019_Mouse,Apoptosis,9/141,0.8081118145904043,0.999993572132612,0,0,0.7791415405620662,0.1659998806571086,NTRK1;CTSO;DIABLO;CTSK;IL3RA;PIK3CD;KRAS;FOS;ACTB
+KEGG_2019_Mouse,Acute myeloid leukemia,4/69,0.8154448094019996,0.999993572132612,0,0,0.703603819761025,0.1435503321956379,EIF4EBP1;PIK3CD;KRAS;DUSP6
+KEGG_2019_Mouse,Melanogenesis,6/100,0.824009828126364,0.999993572132612,0,0,0.7295573245445001,0.1412224699812964,FZD3;FZD6;CAMK2A;KRAS;FZD10;ADCY5
+KEGG_2019_Mouse,Parkinson disease,9/144,0.826878572482603,0.999993572132612,0,0,0.7617021276595745,0.1447976120640755,PRKN;GNAL;ADORA2A;NDUFA3;DRD1;SLC25A31;DRD2;COX7B2;ADCY5
+KEGG_2019_Mouse,Insulin secretion,5/86,0.830653654763277,0.999993572132612,0,0,0.7055994821288205,0.1309185876787474,GLP1R;ADCYAP1;CAMK2A;CCK;ADCY5
+KEGG_2019_Mouse,Hepatitis C,10/160,0.8365631150896315,0.999993572132612,0,0,0.7615529117094553,0.1359016378417757,CDKN1A;CDK6;TRAF3;EIF2AK2;PIK3CD;KRAS;EIF2AK4;IKBKE;IFNAR1;OAS1G
+KEGG_2019_Mouse,Regulation of actin cytoskeleton,14/217,0.8386944653913739,0.999993572132612,0,0,0.7874970236162522,0.138527659614219,INSRR;LPAR2;PIK3CD;SSH2;ACTB;FGF7;CXCL12;APC;ITGA10;PAK6;KRAS;EZR;PAK3;FGF10
+KEGG_2019_Mouse,Spliceosome,8/132,0.8414655964686881,0.999993572132612,0,0,0.7371139220077064,0.1272333444720129,EFTUD2;XAB2;RBM8A;TRA2A;DHX38;TXNL4A;WBP11;RBM22
+KEGG_2019_Mouse,Other types of O-glycan biosynthesis,1/22,0.8417800450189034,0.999993572132612,0,0,0.5447429283045722,0.0938246306563085,POGLUT1
+KEGG_2019_Mouse,Tyrosine metabolism,2/40,0.8426548143314437,0.999993572132612,0,0,0.601901951139531,0.103044336650866,MAOB;FAH
+KEGG_2019_Mouse,Natural killer cell mediated cytotoxicity,7/118,0.8454812587331062,0.999993572132612,0,0,0.7205743243243243,0.1209478792093558,SHC3;PIK3CD;NFATC1;KRAS;CD247;ICAM1;IFNAR1
+KEGG_2019_Mouse,Phenylalanine metabolism,1/23,0.8545068864424096,0.999993572132612,0,0,0.5199535831540812,0.0817526749083017,MAOB
+KEGG_2019_Mouse,Pyrimidine metabolism,3/58,0.855299288146306,0.999993572132612,0,0,0.623600090682385,0.097471080492361,NME6;UPP1;UPB1
+KEGG_2019_Mouse,Cytokine-cytokine receptor interaction,19/292,0.8605260594211094,0.999993572132612,0,0,0.7941428848229856,0.1192892984428598,CCR1;CCL25;AMHR2;IL20RA;GDF1;TNFRSF1B;CSF2RA;BMP5;CD4;CXCL12;IL3RA;CCL27A;TNFRSF8;CCL27B;CCR6;IL12RB1;IL18R1;IFNAR1;BMPR1A
+KEGG_2019_Mouse,Vitamin digestion and absorption,1/24,0.8662105829661755,0.999993572132612,0,0,0.4973198332340679,0.0714286713558842,FOLH1
+KEGG_2019_Mouse,Carbohydrate digestion and absorption,2/43,0.8705295865003504,0.999993572132612,0,0,0.5577691664767115,0.077336665203107,HKDC1;PIK3CD
+KEGG_2019_Mouse,Toxoplasmosis,6/108,0.8745413535046022,0.999993572132612,0,0,0.672043208288937,0.0900912210182268,LAMB3;LAMA4;IGTP;LAMC2;H2-AA;H2-AB1
+KEGG_2019_Mouse,Glycosylphosphatidylinositol (GPI)-anchor biosynthesis,1/25,0.8769733406584865,0.999993572132612,0,0,0.4765722291407223,0.062563775744008,PIGF
+KEGG_2019_Mouse,Human cytomegalovirus infection,16/255,0.8791061907904815,0.999993572132612,0,0,0.7638784059918632,0.098425411751325,CCR1;ARHGEF11;CDKN1A;H2-M5;H2-Q6;H2-Q7;PIK3CD;NFATC1;PTGS2;ADCY5;CXCL12;CDK6;GNG7;EIF4EBP1;KRAS;B2M
+KEGG_2019_Mouse,Cardiac muscle contraction,4/78,0.8820111665972598,0.999993572132612,0,0,0.6177268972028798,0.0775559594240834,TNNT2;CACNA2D2;CACNG3;COX7B2
+KEGG_2019_Mouse,Salmonella infection,4/78,0.8820111665972598,0.999993572132612,0,0,0.6177268972028798,0.0775559594240834,DYNC1I2;FOS;NLRC4;ACTB
+KEGG_2019_Mouse,Mitophagy,3/63,0.891006156587006,0.999993572132612,0,0,0.5714775561097257,0.0659507626169916,PRKN;MFN2;KRAS
+KEGG_2019_Mouse,cGMP-PKG signaling pathway,10/172,0.8928118663716291,0.999993572132612,0,0,0.704677752266982,0.079895938107458,SLC8A3;GUCY1A2;GUCY1A1;NPR1;PDE3A;NFATC1;SLC25A31;ADRA2C;ADRA1A;ADCY5
+KEGG_2019_Mouse,Phosphatidylinositol signaling system,5/98,0.903139306552008,0.999993572132612,0,0,0.6141516652571383,0.0625688299420016,PLCZ1;ITPKA;PIK3CD;IP6K1;DGKI
+KEGG_2019_Mouse,Necroptosis,10/176,0.9075798555982664,0.999993572132612,0,0,0.6875466801457553,0.0666739603233068,CYLD;PLA2G4D;PLA2G4E;PLA2G4B;CAMK2A;SPATA2;EIF2AK2;SLC25A31;JAK3;IFNAR1
+KEGG_2019_Mouse,Leukocyte transendothelial migration,6/115,0.9082062657843176,0.999993572132612,0,0,0.6286437948759089,0.0605281890371315,CXCL12;PIK3CD;THY1;EZR;ACTB;ICAM1
+KEGG_2019_Mouse,RNA degradation,4/83,0.9091041681303804,0.999993572132612,0,0,0.5784723264132915,0.0551258645176528,BTG3;BTG2;CNOT6L;TOB1
+KEGG_2019_Mouse,Relaxin signaling pathway,7/131,0.9097715094532048,0.999993572132612,0,0,0.6445715725806451,0.0609518478176353,SHC3;GNG7;PIK3CD;KRAS;FOS;MAP2K7;ADCY5
+KEGG_2019_Mouse,Cholesterol metabolism,2/49,0.9131183035583184,0.999993572132612,0,0,0.4864055146815139,0.0442093145634796,ABCA1;LIPG
+KEGG_2019_Mouse,Glycolysis / Gluconeogenesis,3/67,0.913653921030535,0.999993572132612,0,0,0.5356433135910225,0.0483704238831489,ALDH2;HKDC1;ALDH7A1
+KEGG_2019_Mouse,Peroxisome,4/84,0.9138077833080348,0.999993572132612,0,0,0.5712102308172177,0.0514860526454391,NUDT7;CAT;PXMP2;DECR2
+KEGG_2019_Mouse,MicroRNAs in cancer,17/281,0.915094624211876,0.999993572132612,0,0,0.7342124070897655,0.065145055001664,MIR29B-2;NOTCH2;FZD3;CDKN1A;BRCA1;PTGS2;THBS1;RASSF1;CCND2;CDK6;APC;CCNE1;TNR;BMF;KRAS;VIM;EZR
+KEGG_2019_Mouse,AGE-RAGE signaling pathway in diabetic complications,5/101,0.9163657709710872,0.999993572132612,0,0,0.5948618913857678,0.0519550476691782,EGR1;PIK3CD;NFATC1;KRAS;ICAM1
+KEGG_2019_Mouse,JAK-STAT signaling pathway,9/164,0.9179567687842668,0.999993572132612,0,0,0.6626912673099439,0.0567296742072027,CDKN1A;CCND2;IL3RA;IL20RA;PIK3CD;IL12RB1;CSF2RA;JAK3;IFNAR1
+KEGG_2019_Mouse,PPAR signaling pathway,4/85,0.9182929989672146,0.999993572132612,0,0,0.5641274462235161,0.0480855286789742,ACADL;SCD4;SCD1;RXRG
+KEGG_2019_Mouse,RIG-I-like receptor signaling pathway,3/68,0.9186033112414786,0.999993572132612,0,0,0.5273738730097832,0.0447745177397343,CYLD;TRAF3;IKBKE
+KEGG_2019_Mouse,Glucagon signaling pathway,5/102,0.920406830283214,0.999993572132612,0,0,0.5886971182928555,0.048826244556757,PRKAB2;CAMK2A;SIK2;ACACB;ACACA
+KEGG_2019_Mouse,NF-kappa B signaling pathway,5/102,0.920406830283214,0.999993572132612,0,0,0.5886971182928555,0.048826244556757,CARD10;CXCL12;TRAF3;PTGS2;ICAM1
+KEGG_2019_Mouse,Cortisol synthesis and secretion,3/69,0.9232923169540244,0.999993572132612,0,0,0.5193550215370665,0.0414494082553681,NR4A1;KCNK2;ADCY5
+KEGG_2019_Mouse,RNA transport,9/167,0.9272647911692458,0.999993572132612,0,0,0.6500015842588045,0.0490855918440948,NXF3;RBM8A;GM9839;POP4;EIF4EBP1;RNPS1;EIF3J2;EIF2S3Y;RPP14
+KEGG_2019_Mouse,Taste transduction,4/88,0.9305200088944888,0.999993572132612,0,0,0.54389091881294,0.0391665095432185,PDE1C;GABRA6;PDE1B;HTR1D
+KEGG_2019_Mouse,Viral carcinogenesis,13/229,0.93155640324002,0.999993572132612,0,0,0.6863150006970584,0.0486587313863019,EGR2;CDKN1A;H2-M5;H2-Q6;H2-Q7;EIF2AK2;PIK3CD;CCND2;CDK6;CCNE1;TRAF3;KRAS;JAK3
+KEGG_2019_Mouse,Pentose phosphate pathway,1/32,0.9316103953845848,0.999993572132612,0,0,0.3688185433656047,0.0261273204115604,RBKS
+KEGG_2019_Mouse,Amoebiasis,5/106,0.934893513109982,0.999993572132612,0,0,0.5652587730683181,0.0380547162218936,GNAL;LAMB3;LAMA4;PIK3CD;LAMC2
+KEGG_2019_Mouse,Bile secretion,3/72,0.9359125413083758,0.999993572132612,0,0,0.4966930499837363,0.0328975927889035,SLCO1A4;SLCO1A6;ADCY5
+KEGG_2019_Mouse,Thermogenesis,13/231,0.9362674202388386,0.999993572132612,0,0,0.6799437109343525,0.0447771069225903,SMARCD1;PRKAB2;NPR1;COX18;NDUFA3;SMARCA2;COX7B2;ACTB;ADCY5;NDUFAF7;NDUFAF5;DPF3;KRAS
+KEGG_2019_Mouse,SNARE interactions in vesicular transport,1/33,0.9371141474330016,0.999993572132612,0,0,0.3572735056039851,0.0232049791876456,VTI1A
+KEGG_2019_Mouse,Starch and sucrose metabolism,1/33,0.9371141474330016,0.999993572132612,0,0,0.3572735056039851,0.0232049791876456,HKDC1
+KEGG_2019_Mouse,Inositol phosphate metabolism,3/73,0.9396737298807566,0.999993572132612,0,0,0.4895707160669754,0.0304623431770438,PLCZ1;ITPKA;PIK3CD
+KEGG_2019_Mouse,Endocrine and other factor-regulated calcium reabsorption,2/55,0.942280771853197,0.999993572132612,0,0,0.4311996708399459,0.0256356783137353,KLK1B22;KLK1B24
+KEGG_2019_Mouse,DNA replication,1/35,0.9468291849987674,0.999993572132612,0,0,0.3362207896857373,0.0183699530427667,RPA3
+KEGG_2019_Mouse,Insulin resistance,5/110,0.9469716526690238,0.999993572132612,0,0,0.5436062065275549,0.0296189930384144,PRKAB2;TBC1D4;PIK3CD;SLC2A4;ACACB
+KEGG_2019_Mouse,Autophagy,6/130,0.9550684914955292,0.999993572132612,0,0,0.5521448288368157,0.0253833247742072,RRAGD;PIK3CD;WIPI1;KRAS;EIF2AK4;IGF1R
+KEGG_2019_Mouse,Adrenergic signaling in cardiomyocytes,7/148,0.9582239603108608,0.999993572132612,0,0,0.5663297872340426,0.0241674153633656,TNNT2;CAMK2A;CACNA2D2;SCN7A;ADRA1A;CACNG3;ADCY5
+KEGG_2019_Mouse,Ribosome biogenesis in eukaryotes,5/115,0.9592008164763244,0.999993572132612,0,0,0.5187549653841789,0.0216086468152412,TBL3;NXF3;POP4;WDR75;MDN1
+KEGG_2019_Mouse,Cytosolic DNA-sensing pathway,2/61,0.9619729227453324,0.999993572132612,0,0,0.3872221342203918,0.0150122054528219,POLR3F;IKBKE
+KEGG_2019_Mouse,Chagas disease (American trypanosomiasis),4/103,0.9701958641756928,0.999993572132612,0,0,0.4611051248605833,0.0139517988783968,GNAL;PIK3CD;FOS;CD247
+KEGG_2019_Mouse,NOD-like receptor signaling pathway,10/205,0.9714852133528356,0.999993572132612,0,0,0.5843649149848273,0.0169052274443534,NLRP1B;NLRP1A;IFI207;TRAF3;TXNIP;MFN2;NLRC4;IKBKE;OAS1G;IFNAR1
+KEGG_2019_Mouse,Endocytosis,14/269,0.972746511759842,0.999993572132612,0,0,0.6251172408699827,0.0172730852466475,SMURF2;H2-M5;H2-Q6;H2-Q7;CBL;IGF1R;RNF41;RUFY1;EHD3;ACAP2;GRK5;BIN1;RAB11FIP3;SNX5
+KEGG_2019_Mouse,Nucleotide excision repair,1/43,0.9728302680218608,0.999993572132612,0,0,0.2720601316491727,0.0074940742325431,RPA3
+KEGG_2019_Mouse,Mineral absorption,1/44,0.9750178767535874,0.999993572132612,0,0,0.2657186712618379,0.0067225423546298,SLC40A1
+KEGG_2019_Mouse,Huntington disease,9/192,0.9752388351047568,0.999993572132612,0,0,0.560434149243932,0.0140516975554542,DNAH3;DNAH8;DCTN4;NDUFA3;DNAH6;SLC25A31;COX7B2;GRIN2B;DNAH7B
+KEGG_2019_Mouse,Systemic lupus erythematosus,6/143,0.976822379561752,0.999993572132612,0,0,0.4993959067553581,0.0117110564188021,C4B;GRIN2A;H3F3B;GRIN2B;H2-AA;H2-AB1
+KEGG_2019_Mouse,Cysteine and methionine metabolism,1/50,0.9849040300405923,0.999993572132612,0,0,0.2331054464126871,0.0035457841923119,AMD2
+KEGG_2019_Mouse,N-Glycan biosynthesis,1/50,0.9849040300405923,0.999993572132612,0,0,0.2331054464126871,0.0035457841923119,MGAT3
+KEGG_2019_Mouse,mRNA surveillance pathway,3/96,0.9859298084811848,0.999993572132612,0,0,0.3680315340680556,0.0052150491858283,NXF3;RBM8A;RNPS1
+KEGG_2019_Mouse,Pertussis,2/76,0.986979645887388,0.999993572132612,0,0,0.3084785720299739,0.0040428775834213,C4B;FOS
+KEGG_2019_Mouse,Glycosaminoglycan biosynthesis,1/53,0.9882656813431836,0.999993572132612,0,0,0.2196211322923651,0.0025923439669106,B4GALT4
+KEGG_2019_Mouse,Tuberculosis,7/178,0.9907455746093736,0.999993572132612,0,0,0.4662061403508772,0.004334557943948,CD74;CAMK2A;RFXANK;FCGR2B;H2-AA;CD209A;H2-AB1
+KEGG_2019_Mouse,Sphingolipid signaling pathway,4/124,0.9916000325037968,0.999993572132612,0,0,0.3799750467872738,0.0032052590044654,CERS4;CERS5;PIK3CD;KRAS
+KEGG_2019_Mouse,Pancreatic secretion,3/105,0.9922540886150362,0.999993572132612,0,0,0.3353931348100337,0.0026080394132187,BST1;CCK;ADCY5
+KEGG_2019_Mouse,Legionellosis,1/58,0.9922892552731756,0.999993572132612,0,0,0.2003015009503834,0.00155045905092,NLRC4
+KEGG_2019_Mouse,Tight junction,6/167,0.993728895850056,0.999993572132612,0,0,0.4243931393810545,0.0026697936316106,PRKAB2;MYH8;ARHGEF18;EZR;MAP2K7;ACTB
+KEGG_2019_Mouse,Non-alcoholic fatty liver disease (NAFLD),5/151,0.9946949200930512,0.999993572132612,0,0,0.3900731961760129,0.0020748780514127,PRKAB2;NDUFA3;PIK3CD;CYP2E1;COX7B2
+KEGG_2019_Mouse,Alzheimer disease,6/175,0.9960271000222104,0.999993572132612,0,0,0.4041261194002269,0.0016087504733976,GRIN2A;NDUFA3;PSEN1;COX7B2;GRIN2B;RYR3
+KEGG_2019_Mouse,Aminoacyl-tRNA biosynthesis,1/66,0.996061931923264,0.999993572132612,0,0,0.1755723728326468,0.0006927809629995368,CARS2
+KEGG_2019_Mouse,Protein processing in endoplasmic reticulum,5/163,0.997436705130092,0.999993572132612,0,0,0.3602103383428942,0.0009245107160064094,PRKN;DNAJC5B;EIF2AK2;EIF2AK4;MAP2K7
+KEGG_2019_Mouse,Oxidative phosphorylation,3/134,0.9989471412915992,0.999993572132612,0,0,0.2607317583902838,0.00027465831596297323,ATP6V1G2;NDUFA3;COX7B2
+KEGG_2019_Mouse,Ribosome,4/170,0.9995797841330062,0.999993572132612,0,0,0.2739892821441724,0.00011515884122431072,RPL3;RPL13A;MRPL9;MRPS6
+KEGG_2019_Mouse,Olfactory transduction,7/1133,0.999993572132612,0.999993572132612,0,0,0.0670898090586145,4.312457817135802e-07,SLC8A3;GNAL;PDE1C;PDE1B;GNG7;CAMK2A;CNGA2

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/enrich_5xfad_kegg.csv

@@ -0,0 +1,295 @@
+Gene_set,Term,Overlap,P-value,Adjusted P-value,Old P-value,Old Adjusted P-value,Odds Ratio,Combined Score,Genes
+KEGG_2019_Mouse,Osteoclast differentiation,55/128,1.449195926454791e-19,4.260636023777085e-17,0,0,5.930528375733855,257.255099773218,SPI1;NCF1;CSF1;NCF2;NCF4;FHL2;TREM2;TNF;PPP3CA;PPP3CC;AKT2;BLNK;AKT1;IFNAR2;MAP2K1;IL1R1;IFNGR1;IFNGR2;CYBA;FOS;TGFBR1;TNFRSF1A;TGFBR2;FCGR1;IL1A;FCGR3;TYROBP;FCGR4;BTK;LCP2;IRF9;CSF1R;PIK3R3;TNFRSF11A;PIRB;LILRA5;SOCS3;MAPK9;PPP3R1;MAPK8;SOCS1;PLCG2;MAPK1;STAT1;STAT2;NFATC1;LILRB4A;NFKB1;NFKB2;FOSL2;MAPK11;TEC;FOSB;FCGR2B;MAP3K14
+KEGG_2019_Mouse,Chagas disease (American trypanosomiasis),45/103,1.3359970736741422e-16,1.963915698300989e-14,0,0,6.085172413793104,222.4232967396207,C1QB;C1QA;CCL12;PIK3R3;CD3G;CD3E;TNF;CD3D;GNAI1;GNAI2;C3;GNA14;MAPK9;GNA15;MAPK8;CASP8;PPP2R1A;AKT2;GNA11;CCL5;BDKRB2;CCL3;AKT1;CCL2;MAPK1;MAP2K4;IFNGR1;IFNGR2;IRAK4;FOS;CFLAR;NFKB1;TGFBR1;TNFRSF1A;TGFBR2;MAPK11;PPP2R2C;PPP2R2B;FAS;TLR6;TLR4;PLCB2;MYD88;TLR2;C1QC
+KEGG_2019_Mouse,Epstein-Barr virus infection,69/229,1.6963168250434842e-14,1.6623904885426145e-12,0,0,3.399048180592992,107.77610867958644,H2-T23;H2-T22;TRADD;H2-K1;CD3G;CD3E;CD3D;TNF;ICAM1;CASP8;MYC;AKT2;BLNK;H2-OB;AKT1;H2-OA;B2M;JAK3;IFNAR2;MAP2K4;ENTPD1;TAP2;TAP1;IRAK4;TAPBP;OAS2;OAS3;BTK;IRF7;H2-D1;CD44;IRF9;TLR2;HDAC2;H2-DMA;H2-Q6;H2-M3;H2-Q7;H2-Q4;PIK3R3;OAS1A;OAS1B;OAS1G;MAPK9;MAPK8;H2-DMB1;PLCG2;LYN;H2-EB1;STAT1;STAT2;STAT3;EIF2AK2;ISG15;H2-AA;NFKB1;NFKB2;CXCL10;MAPK11;MAVS;CDK6;CDK2;FAS;VIM;NFKBIE;MAP3K14;MYD88;H2-Q10;H2-AB1
+KEGG_2019_Mouse,Tuberculosis,58/178,4.925701686693233e-14,3.6203907397195256e-12,0,0,3.7994710177320816,116.42234441039314,ITGAM;TRADD;ITGB2;LSP1;TCIRG1;TNF;CTSS;MRC2;PPP3CA;CASP8;PPP3CC;AKT2;LAMP2;H2-OB;ITGAX;AKT1;LBP;H2-OA;CTSD;FCER1G;RIPK2;IFNGR1;IFNGR2;IRAK4;TNFRSF1A;FCGR1;TLR1;IL1A;FCGR3;FCGR4;TLR6;ATP6V0D2;TLR4;TLR2;H2-DMA;CEBPG;C3;MAPK9;PPP3R1;MAPK8;CLEC7A;H2-DMB1;MAPK1;CD14;CAMK2G;CD74;H2-EB1;NFYA;IL10RB;STAT1;IL10RA;CARD9;H2-AA;NFKB1;MAPK11;FCGR2B;MYD88;H2-AB1
+KEGG_2019_Mouse,Leishmaniasis,32/67,1.5442486922035986e-13,9.08018231015716e-12,0,0,7.138943248532289,210.5921773353569,ITGAM;NCF1;NCF2;H2-DMA;ITGB2;NCF4;TNF;C3;H2-DMB1;H2-OB;MAPK1;H2-OA;H2-EB1;IFNGR1;STAT1;IFNGR2;CYBB;CYBA;IRAK4;FOS;H2-AA;NFKB1;FCGR1;IL1A;MAPK11;FCGR3;FCGR4;PTPN6;TLR4;MYD88;TLR2;H2-AB1
+KEGG_2019_Mouse,Lysosome,45/124,4.606256207081136e-13,2.061135632507806e-11,0,0,4.462278481012659,126.75633386491732,SCARB2;CD63;ASAH1;IDUA;HEXB;CTSZ;HEXA;CTSW;TCIRG1;NAGPA;LIPA;GNS;LITAF;CTSS;CLN5;AP3M2;CLN3;NAGLU;GM2A;CTSL;LAMP2;CTSH;ARSG;AGA;CTSE;GUSB;CTSD;CTSC;CTSB;CTSA;CD164;HGSNAT;DNASE2A;SLC11A1;FUCA1;LAPTM5;NAGA;PLA2G15;NPC2;GLB1;TPP1;MAN2B1;CD68;ATP6V0D2;LGMN
+KEGG_2019_Mouse,Th17 cell differentiation,40/102,4.907465791685252e-13,2.061135632507806e-11,0,0,5.047707603175739,143.06641203264277,H2-DMA;IL4RA;CD3G;IL6RA;GATA3;CD3E;IL2RG;HIF1A;CD3D;IL27RA;PPP3CA;MAPK9;PPP3R1;MAPK8;PPP3CC;H2-DMB1;H2-OB;IL21R;MAPK1;H2-OA;JAK3;H2-EB1;HSP90AA1;IL1R1;IFNGR1;STAT1;IFNGR2;STAT3;NFATC1;FOS;H2-AA;NFKB1;TGFBR1;RUNX1;TGFBR2;MAPK11;IL2RB;NFKBIE;LAT;H2-AB1
+KEGG_2019_Mouse,Influenza A,54/168,6.78550700709852e-13,2.493673825108706e-11,0,0,3.718241386599028,104.18072569326512,TNF;ICAM1;PYCARD;IFIH1;AKT2;H2-OB;AKT1;TRIM25;H2-OA;IFNAR2;MAP2K4;MAP2K1;RSAD2;IFNGR1;IFNGR2;IRAK4;TNFRSF1A;IL1A;OAS2;OAS3;IRF7;VDAC1;TLR7;TLR4;IRF9;CCL12;H2-DMA;PIK3R3;OAS1A;OAS1B;OAS1G;SOCS3;MAPK9;MAPK8;H2-DMB1;CCL5;NLRP3;CCL2;MAPK1;FDPS;IL33;H2-EB1;STAT1;MX2;STAT2;EIF2AK2;H2-AA;NFKB1;CXCL10;MAPK11;MAVS;FAS;MYD88;H2-AB1
+KEGG_2019_Mouse,Pertussis,31/76,6.127998809193345e-11,2.0018129443364923e-09,0,0,5.373576756968983,126.36270847542912,C1QB;C1QA;ITGAM;ITGB2;NOD1;TNF;CXCL5;GNAI1;GNAI2;C2;PYCARD;C4B;C3;MAPK9;MAPK8;NLRP3;MAPK1;CD14;TICAM2;IRAK4;FOS;NFKB1;IL1A;MAPK11;IRF1;IRF8;SERPING1;ITGA5;TLR4;MYD88;C1QC
+KEGG_2019_Mouse,Human T-cell leukemia virus 1 infection,64/245,1.4772745412328308e-10,4.343187151224523e-09,0,0,2.777378526092652,62.867773972358655,H2-T23;H2-T22;SPI1;H2-K1;ITGB2;CD3G;CD3E;CD3D;TNF;ICAM1;CDC20;PPP3CA;ZFP36;PPP3CC;CHEK2;MYC;AKT2;H2-OB;AKT1;TSPO;H2-OA;B2M;JAK3;MAP2K4;MAP2K1;IL1R1;FOS;TGFBR1;TNFRSF1A;TGFBR2;VDAC1;TLN1;H2-D1;H2-DMA;H2-Q6;H2-M3;H2-Q7;H2-Q4;PIK3R3;ADCY2;IL2RG;ADCY7;CCNB2;MAPK9;PPP3R1;MAPK8;H2-DMB1;MAPK1;EGR1;FDPS;EGR2;H2-EB1;NFATC1;H2-AA;NFKB1;NFKB2;DLG1;CDK2;IL2RB;KRAS;LTBR;MAP3K14;H2-Q10;H2-AB1
+KEGG_2019_Mouse,Hematopoietic cell lineage,34/94,3.5698250419172645e-10,8.233749362066459e-09,0,0,4.422305764411028,96.19989586116512,CSF1R;CSF3R;ITGAM;CSF1;H2-DMA;IL4RA;CD3G;IL6RA;SIGLECH;CD3E;TNF;CD3D;CSF2RA;H2-DMB1;H2-OB;CD38;CD37;H2-OA;CD14;CD33;H2-EB1;MME;IL1R1;H2-AA;FCGR1;IL1A;GP9;CD5;IL3RA;CD9;ITGA5;CD44;CD22;H2-AB1
+KEGG_2019_Mouse,B cell receptor signaling pathway,29/72,3.624126834605312e-10,8.233749362066459e-09,0,0,5.256655514275745,114.27042625557168,CD81;PIK3R3;PIRB;RASGRP3;PPP3CA;PPP3R1;PPP3CC;AKT2;INPP5D;BLNK;RAC2;PLCG2;AKT1;MAPK1;LYN;MAP2K1;CD72;NFATC1;FOS;VAV1;NFKB1;BTK;PTPN6;KRAS;FCGR2B;NFKBIE;PIK3AP1;CARD11;CD22
+KEGG_2019_Mouse,Toxoplasmosis,37/108,3.6407735274443517e-10,8.233749362066459e-09,0,0,4.069773824523759,88.45105926703117,H2-DMA;TNF;PIK3CG;GNAI1;PIK3R5;GNAI2;MAPK9;MAPK8;SOCS1;CASP8;AKT2;ALOX5;H2-DMB1;H2-OB;AKT1;MAPK1;H2-OA;CCR5;H2-EB1;IFNGR1;IL10RB;STAT1;IFNGR2;IL10RA;STAT3;IRGM1;IRAK4;H2-AA;NFKB1;TNFRSF1A;MAPK11;IGTP;TLR4;MYD88;H2-AB1;BIRC3;TLR2
+KEGG_2019_Mouse,Phagosome,51/180,5.03709293625993e-10,1.0577895166145854e-08,0,0,3.0965468639887246,66.2940393549671,H2-T23;H2-T22;ITGAM;NCF1;ITGB5;NCF2;H2-K1;NCF4;ITGB2;TCIRG1;CTSS;SEC61A2;MRC2;TUBA1C;CTSL;LAMP2;H2-OB;OLR1;H2-OA;TAP2;TAP1;CYBB;CYBA;FCGR1;FCGR3;FCGR4;ITGA5;TLR6;RAB7B;ATP6V0D2;TLR4;H2-D1;ATP6V0E;TLR2;H2-DMA;H2-Q6;H2-M3;H2-Q7;H2-Q4;THBS1;THBS3;C3;CLEC7A;H2-DMB1;CD14;DYNC1I1;H2-EB1;H2-AA;FCGR2B;H2-Q10;H2-AB1
+KEGG_2019_Mouse,NOD-like receptor signaling pathway,55/205,9.914743468875566e-10,1.943289719899611e-08,0,0,2.8735863095238097,59.57469723098678,ANTXR2;TNF;PYCARD;CASP12;CASP8;CASP4;CTSB;IFNAR2;GBP5;HSP90AA1;GBP7;RIPK3;RIPK2;PRKCD;CYBB;CYBA;IRAK4;NAIP2;NAIP5;AIM2;NAIP6;OAS2;OAS3;IRF7;VDAC1;TLR4;PLCB2;IRF9;BIRC3;CCL12;OAS1A;OAS1B;NOD1;OAS1G;MAPK9;MAPK8;CCL5;NLRP3;CCL2;MAPK1;GBP2;GBP3;GSDMD;IFI207;IFI204;STAT1;STAT2;CARD9;ERBIN;NFKB1;P2RX7;MAPK11;MAVS;MYD88;MCU
+KEGG_2019_Mouse,Toll-like receptor signaling pathway,34/99,1.731253545741801e-09,3.18117839030056e-08,0,0,4.080971659919029,82.3312366748842,CD86;CXCL9;PIK3R3;TNF;MAPK9;MAPK8;CASP8;AKT2;CCL5;CCL4;SPP1;CCL3;AKT1;MAPK1;CD14;LBP;IFNAR2;MAP2K4;MAP2K1;TICAM2;STAT1;IRAK4;FOS;NFKB1;TLR1;CXCL10;MAPK11;IRF7;IRF5;TLR7;TLR6;TLR4;MYD88;TLR2
+KEGG_2019_Mouse,Human immunodeficiency virus 1 infection,60/238,2.4362876207802884e-09,4.2133444735847346e-08,0,0,2.6433903928813813,52.42580766856003,H2-T23;H2-T22;TRIM30D;TRADD;H2-K1;CGAS;CD3G;CD3E;CD3D;TNF;PPP3CA;GNGT2;PPP3CC;CASP8;AKT2;RAC2;AKT1;CCR5;B2M;APOBEC3;MAP2K1;TAP2;TAP1;FOS;IRAK4;TNFRSF1B;TNFRSF1A;TAPBP;TLR4;H2-D1;TLR2;TRIM12C;H2-Q6;H2-M3;H2-Q7;H2-Q4;PIK3R3;GNAI1;GNAI2;CCNB2;MAPK9;PPP3R1;PAK1;MAPK8;GNG5;GNA11;PLCG2;MAPK1;NFATC1;PTK2;NFKB1;BST2;CUL4A;MAPK11;GNB1;CDK1;FAS;KRAS;MYD88;H2-Q10
+KEGG_2019_Mouse,Th1 and Th2 cell differentiation,31/87,3.2267333629018043e-09,5.2703311594062814e-08,0,0,4.315363137304392,84.37309780278794,MAML2;H2-DMA;IL4RA;CD3G;GATA3;CD3E;IL2RG;CD3D;PPP3CA;MAPK9;PPP3R1;MAPK8;PPP3CC;H2-DMB1;H2-OB;MAPK1;H2-OA;JAK3;H2-EB1;IFNGR1;STAT1;IFNGR2;NFATC1;FOS;H2-AA;NFKB1;MAPK11;IL2RB;NFKBIE;LAT;H2-AB1
+KEGG_2019_Mouse,Kaposi sarcoma-associated herpesvirus infection,55/216,7.621719050524273e-09,1.1793607372916507e-07,0,0,2.6755767524401066,50.01258672930052,CD86;H2-T23;H2-T22;TRADD;H2-K1;FGF2;PIK3CG;ICAM1;PPP3CA;ZFP36;GNGT2;PPP3CC;CASP8;MYC;AKT2;AKT1;CCR5;IFNAR2;MAP2K4;MAP2K1;IFNGR1;FOS;TNFRSF1A;HCK;IRF7;H2-D1;IRF9;H2-Q6;H2-M3;H2-Q7;H2-Q4;PIK3R3;HIF1A;PIK3R5;C3;MAPK9;PPP3R1;MAPK8;GNG5;PLCG2;MAPK1;LYN;ANGPT2;STAT1;STAT2;STAT3;EIF2AK2;NFATC1;NFKB1;MAPK11;CDK6;GNB1;FAS;KRAS;H2-Q10
+KEGG_2019_Mouse,Chemokine signaling pathway,51/197,1.4637176414593147e-08,2.151664932945193e-07,0,0,2.733343711083437,49.30870386548998,CXCL9;NCF1;CXCL13;CXCL5;PIK3CG;CXCL16;GNGT2;AKT2;RAC2;AKT1;CCR5;JAK3;MAP2K1;PRKCD;VAV1;FGR;HCK;XCL1;DOCK2;PLCB2;CX3CR1;CCL12;SHC1;WAS;PIK3R3;ADCY2;CXCR6;PRKCZ;ADCY7;GNAI1;PIK3R5;GNAI2;PAK1;CCL9;GNG5;CCL6;CXCR3;CCL5;CCL4;CCL3;CCL2;MAPK1;LYN;STAT1;STAT2;STAT3;PTK2;NFKB1;CXCL10;GNB1;KRAS
+KEGG_2019_Mouse,Measles,41/144,2.09636997969844e-08,2.82401886633968e-07,0,0,3.1085276660263093,54.960240948712105,TRADD;PIK3R3;OAS1A;CD3G;OAS1B;CD3E;IL2RG;CD3D;OAS1G;IFIH1;MAPK9;MAPK8;CASP8;AKT2;AKT1;JAK3;IFNAR2;STAT1;MX2;STAT2;STAT3;MSN;EIF2AK2;IRAK4;FOS;NFKB1;IL1A;MAVS;CDK6;OAS2;OAS3;CDK2;IL2RB;IRF7;FAS;TLR7;FCGR2B;TLR4;MYD88;IRF9;TLR2
+KEGG_2019_Mouse,Natural killer cell mediated cytotoxicity,36/118,2.1132113965807133e-08,2.82401886633968e-07,0,0,3.424934408706637,60.52705731851268,H2-T23;SHC1;H2-K1;ITGB2;PIK3R3;TNF;ICAM1;PPP3CA;PPP3R1;PAK1;KLRB1C;PPP3CC;SH3BP2;PLCG2;RAC2;MAPK1;CD244A;IFNAR2;MAP2K1;FCER1G;IFNGR1;IFNGR2;GZMB;NFATC1;VAV1;TYROBP;FCGR4;FAS;LCP2;CD48;PTPN6;KRAS;ULBP1;HCST;H2-D1;LAT
+KEGG_2019_Mouse,Antigen processing and presentation,30/90,3.368390420309606e-08,4.305681667700106e-07,0,0,3.89514348785872,67.02079602039527,H2-T23;H2-T22;H2-DMA;H2-Q6;H2-K1;H2-Q7;H2-M3;H2-Q4;IFI30;TNF;CTSS;CTSL;H2-DMB1;H2-OB;H2-OA;B2M;CTSB;CD74;HSP90AA1;H2-EB1;NFYA;TAP2;TAP1;H2-AA;TAPBP;PSME1;H2-D1;H2-Q10;LGMN;H2-AB1
+KEGG_2019_Mouse,Cytokine-cytokine receptor interaction,66/292,4.273095848805671e-08,5.234542414786947e-07,0,0,2.290034421562906,38.85808761472586,CNTF;IL1RN;CXCL9;CSF3R;TNFRSF13B;CSF1;CXCL13;TNF;CXCL5;IL27RA;CXCL16;TNFSF13B;IL18RAP;CCR5;IL13RA1;ACVR1;IFNAR2;IL1R1;IFNGR1;IFNGR2;IL16;OSMR;TNFRSF1B;PRLR;TGFBR1;TNFRSF1A;TGFBR2;CSF2RB2;IL1A;IL3RA;XCL1;CX3CR1;CSF1R;CCL12;IL4RA;IL6RA;CSF2RB;CXCR6;TNFRSF11A;IL2RG;CSF2RA;IL1RL1;IL1RL2;CCL9;ACVR1C;CCL6;CXCR3;CCL5;CCL4;IL21R;CCL3;TNFRSF17;CCL2;GDF10;IL33;IL10RB;IL10RA;IL34;OSM;CXCL10;IL2RB;TNFSF9;FAS;TNFSF8;LTBR;CRLF2
+KEGG_2019_Mouse,C-type lectin receptor signaling pathway,34/112,5.9194598211764906e-08,6.961284749703553e-07,0,0,3.39830345093503,56.55604614672088,PIK3R3;LSP1;TNF;PYCARD;PPP3CA;MAPK9;PPP3R1;PAK1;MAPK8;CASP8;PPP3CC;CLEC7A;AKT2;PLCG2;AKT1;NLRP3;MAPK1;CLEC1B;EGR2;EGR3;FCER1G;STAT1;STAT2;PRKCD;CARD9;NFATC1;NFKB1;NFKB2;MAPK11;IRF1;BCL3;KRAS;MAP3K14;IRF9
+KEGG_2019_Mouse,Rheumatoid arthritis,28/84,9.567173932568285e-08,1.0818265908365675e-06,0,0,3.89258932509925,62.91336339033679,CD86;CCL12;CSF1;H2-DMA;ITGB2;TCIRG1;TNFRSF11A;TNF;CXCL5;TNFSF13B;ICAM1;CTSL;H2-DMB1;CCL5;H2-OB;CCL3;CCL2;H2-OA;H2-EB1;ANGPT1;FOS;H2-AA;IL1A;TLR4;ATP6V0D2;TLR2;ATP6V0E;H2-AB1
+KEGG_2019_Mouse,Apoptosis,39/141,1.0780081767274277e-07,1.1738311257698655e-06,0,0,2.983403733833959,47.86268820420485,HRK;TRADD;CTSZ;PIK3R3;CSF2RB;CTSW;TNF;CTSS;TUBA1C;MAPK9;MAPK8;CASP8;CASP12;CASP6;CTSL;AKT2;AKT1;CTSH;MAPK1;BCL2A1D;BCL2A1A;BCL2A1B;CTSD;CTSC;CTSB;MAP2K1;DFFA;PARP3;GZMB;FOS;CFLAR;NFKB1;TNFRSF1A;CSF2RB2;IL3RA;FAS;KRAS;MAP3K14;BIRC3
+KEGG_2019_Mouse,TNF signaling pathway,33/110,1.260627660432872e-07,1.3236590434545152e-06,0,0,3.3399014778325125,53.0592977711344,CCL12;CSF1;TRADD;PIK3R3;TNF;ICAM1;SOCS3;MAPK9;BAG4;MAPK8;CASP8;AKT2;CCL5;AKT1;CCL2;MAPK1;GM5431;MAP2K4;MAP2K1;RIPK3;IFI47;FOS;CFLAR;TNFRSF1B;NFKB1;TNFRSF1A;CXCL10;MAPK11;IRF1;BCL3;FAS;MAP3K14;BIRC3
+KEGG_2019_Mouse,AGE-RAGE signaling pathway in diabetic complications,31/101,1.6995391227420578e-07,1.6655483402872166e-06,0,0,3.4495519939424533,53.77071456488743,CCL12;PIK3R3;TNF;PRKCZ;ICAM1;MAPK9;MAPK8;AKT2;PLCG2;AKT1;PLCE1;CCL2;MAPK1;EGR1;STAT1;PRKCD;STAT3;CYBB;NFATC1;NFKB1;TGFBR1;AGT;TGFBR2;IL1A;MAPK11;COL4A4;COL4A3;KRAS;PLCD3;PLCB2;PLCD4
+KEGG_2019_Mouse,T cell receptor signaling pathway,31/101,1.6995391227420578e-07,1.6655483402872166e-06,0,0,3.4495519939424533,53.77071456488743,PIK3R3;CD3G;CD3E;TNF;CD3D;PPP3CA;MAPK9;PPP3R1;PAK1;PPP3CC;AKT2;NCK2;AKT1;MAPK1;MAP2K1;NFATC1;FOS;NFKB1;VAV1;MAPK11;DLG1;PTPRC;TEC;LCP2;PTPN6;KRAS;PDCD1;NFKBIE;MAP3K14;CARD11;LAT
+KEGG_2019_Mouse,Prolactin signaling pathway,25/72,1.8805455835548838e-07,1.7834851663391477e-06,0,0,4.137688630612054,64.07845446855588,SHC1;LHB;PIK3R3;TNFRSF11A;SOCS2;SOCS3;MAPK9;MAPK8;SOCS1;AKT2;AKT1;MAPK1;CGA;SOCS5;MAP2K1;CISH;STAT1;STAT3;FOS;PRLR;NFKB1;MAPK11;TH;IRF1;KRAS
+KEGG_2019_Mouse,NF-kappa B signaling pathway,31/102,2.175516924916217e-07,1.9097108476668773e-06,0,0,3.400773901358682,52.17069207197015,TRADD;TNFRSF11A;TNF;ICAM1;TNFSF13B;PLAU;CCL4;PLCG2;BLNK;TRIM25;CD14;BCL2A1D;LBP;BCL2A1A;BCL2A1B;LYN;TICAM2;IL1R1;IRAK4;CFLAR;NFKB1;NFKB2;TNFRSF1A;BTK;LTBR;MAP3K14;TLR4;CARD11;MYD88;LAT;BIRC3
+KEGG_2019_Mouse,Human cytomegalovirus infection,58/255,2.204486466663897e-07,1.9097108476668773e-06,0,0,2.3042644586091323,35.31884636779297,H2-T23;H2-T22;TRADD;H2-K1;CGAS;TNF;PPP3CA;GNGT2;PPP3CC;CASP8;MYC;AKT2;RAC2;AKT1;CCR5;B2M;MAP2K1;IL1R1;TAP2;TAP1;TNFRSF1A;TAPBP;PLCB2;H2-D1;PTGER4;CCL12;H2-Q6;H2-M3;H2-Q7;H2-Q4;PIK3R3;ADCY2;IL6RA;ADCY7;GNAI1;GNAI2;PPP3R1;GNG5;GNA11;CCL5;GNA12;CCL4;EIF4EBP1;CCL3;CCL2;MAPK1;IL10RB;IL10RA;STAT3;NFATC1;PTK2;NFKB1;MAPK11;CDK6;GNB1;FAS;KRAS;H2-Q10
+KEGG_2019_Mouse,Fc gamma R-mediated phagocytosis,28/87,2.2085091435603347e-07,1.9097108476668773e-06,0,0,3.694033031034818,56.613930005908514,NCF1;ARPC1B;ASAP3;WAS;PIK3R3;PAK1;AKT2;INPP5D;RAC2;PLCG2;AKT1;MAPK1;LYN;VASP;MAP2K1;GSN;PRKCD;PLA2G4A;VAV1;FCGR1;HCK;PTPRC;AMPH;DOCK2;FCGR2B;PLPP2;PLPP1;LAT
+KEGG_2019_Mouse,Hepatitis C,41/160,4.937415170994045e-07,4.147428743634998e-06,0,0,2.6881286676161147,39.03499836455897,CD81;TRADD;PIK3R3;OAS1A;OAS1B;IFIT1;TNF;OAS1G;SOCS3;CASP8;PPP2R1A;MYC;AKT2;AKT1;MAPK1;IFNAR2;MAP2K1;RSAD2;STAT1;MX2;STAT2;STAT3;EIF2AK2;CFLAR;YWHAZ;NFKB1;TNFRSF1A;CLDN11;CXCL10;MAVS;CDK6;PPP2R2C;OAS2;CLDN14;PPP2R2B;OAS3;CDK2;IRF7;FAS;KRAS;IRF9
+KEGG_2019_Mouse,Fc epsilon RI signaling pathway,23/68,9.793156591002146e-07,7.997744549318419e-06,0,0,3.972809076682316,54.96942245232166,LYN;MAP2K4;MAP2K1;FCER1G;PLA2G4A;PIK3R3;TNF;VAV1;MAPK9;MAPK11;MAPK8;AKT2;ALOX5;INPP5D;ALOX5AP;RAC2;BTK;PLCG2;AKT1;MAPK1;LCP2;KRAS;LAT
+KEGG_2019_Mouse,Salmonella infection,25/78,1.0577365286207356e-06,8.404717281472871e-06,0,0,3.668024270634195,50.46973716004025,PKN3;ARPC1B;WAS;KLC1;PYCARD;KLC4;MAPK9;MAPK8;CCL4;CCL3;MAPK1;LBP;FLNC;CD14;DYNC1I1;IFNGR1;IFNGR2;RHOG;FOS;NFKB1;IL1A;MAPK11;RAB7B;TLR4;MYD88
+KEGG_2019_Mouse,Inflammatory bowel disease (IBD),21/59,1.1102245111098808e-06,8.58963174385013e-06,0,0,4.293466185252048,58.86749289735749,H2-EB1;IFNGR1;STAT1;IFNGR2;H2-DMA;STAT3;IL4RA;NFATC1;IL2RG;TNF;H2-AA;NFKB1;IL1A;IL18RAP;H2-DMB1;H2-OB;IL21R;H2-OA;TLR4;TLR2;H2-AB1
+KEGG_2019_Mouse,Type I diabetes mellitus,23/69,1.3104366721833414e-06,9.878676451843653e-06,0,0,3.886223591549296,52.6394820202925,CD86;H2-T23;H2-T22;H2-EB1;H2-DMA;H2-Q6;GAD1;H2-K1;H2-Q7;H2-M3;ICA1;H2-Q4;GZMB;TNF;H2-AA;IL1A;H2-DMB1;H2-OB;FAS;H2-OA;H2-D1;H2-Q10;H2-AB1
+KEGG_2019_Mouse,MAPK signaling pathway,61/294,2.9273622451389014e-06,2.1516112501770925e-05,0,0,2.047544580248289,26.08860252915948,CSF1;TRADD;FGF1;TNF;FGF2;RPS6KA3;PPP3CA;PPP3CC;MYC;AKT2;RPS6KA1;RAC2;AKT1;DUSP4;MAP4K1;DUSP5;MAP2K4;MAP2K1;IL1R1;DUSP1;CACNA2D1;PLA2G4A;FOS;IRAK4;DUSP8;TGFBR1;DUSP7;TNFRSF1A;TGFBR2;IL1A;CACNB3;PPM1B;CACNB4;MAPKAPK3;CSF1R;NLK;RASGRP3;MAPK9;PPP3R1;PAK1;MAPK8;PDGFD;MKNK1;GNA12;MAPK1;CD14;FLNC;CACNA1S;ANGPT2;ANGPT1;BDNF;NFATC1;IGF1;NFKB1;NFKB2;MAPK11;EFNA3;FAS;KRAS;MAP3K14;MYD88
+KEGG_2019_Mouse,Complement and coagulation cascades,26/88,3.629559051090015e-06,2.602659417123084e-05,0,0,3.260765720297417,40.84565373160644,C1QB;C1QA;ITGAM;CFH;PROS1;ITGB2;C5AR1;TFPI;CLU;C2;C4B;C3;C7;PLAU;C3AR1;BDKRB2;ITGAX;A2M;SERPIND1;SERPINF2;PLAUR;PROCR;F9;SERPING1;MASP1;C1QC
+KEGG_2019_Mouse,Graft-versus-host disease,21/64,4.979888808940495e-06,3.485922166258346e-05,0,0,3.793152113885992,46.31477798510805,CD86;H2-T23;H2-T22;H2-EB1;H2-DMA;H2-Q6;H2-K1;H2-Q7;H2-M3;H2-Q4;GZMB;TNF;H2-AA;IL1A;H2-DMB1;H2-OB;FAS;H2-OA;H2-D1;H2-Q10;H2-AB1
+KEGG_2019_Mouse,HIF-1 signaling pathway,28/104,1.1264203432419908e-05,7.701571649142913e-05,0,0,2.864973417221925,32.64316531964204,PIK3R3;TRF;IL6RA;ENO2;HIF1A;HK2;HK3;AKT2;MKNK1;EIF4EBP1;PLCG2;AKT1;HMOX1;MAPK1;TIMP1;CAMK2G;MAP2K1;PDHA1;ANGPT2;ANGPT1;IFNGR1;IFNGR2;STAT3;CYBB;IGF1;NFKB1;LTBR;TLR4
+KEGG_2019_Mouse,Allograft rejection,20/63,1.4539416942254128e-05,9.714974047778894e-05,0,0,3.610937899309993,40.2209632742941,CD86;H2-T23;H2-T22;H2-EB1;H2-DMA;H2-Q6;H2-K1;H2-Q7;H2-M3;H2-Q4;GZMB;TNF;H2-AA;H2-DMB1;H2-OB;FAS;H2-OA;H2-D1;H2-Q10;H2-AB1
+KEGG_2019_Mouse,Cell adhesion molecules (CAMs),39/170,1.6287195140493054e-05,0.00010439136241546832,0,0,2.319128634075037,25.568697768982656,CD86;CD274;H2-T23;H2-T22;ITGAM;SELPLG;SDC4;H2-DMA;H2-Q6;H2-K1;H2-M3;ITGB2;H2-Q7;H2-Q4;F11R;VSIR;ICAM1;MPZ;H2-DMB1;H2-OB;H2-OA;LRRC4C;MPZL1;H2-EB1;NEGR1;GLYCAM1;ICOSL;H2-AA;CLDN11;MAG;PTPRC;CD6;CLDN14;PDCD1;SIGLEC1;H2-D1;H2-Q10;CD22;H2-AB1
+KEGG_2019_Mouse,JAK-STAT signaling pathway,38/164,1.6362580096637664e-05,0.00010439136241546832,0,0,2.3489602014192177,25.886747684943916,CNTF;CSF3R;IL4RA;PIK3R3;CSF2RB;IL6RA;IL2RG;CSF2RA;IL27RA;SOCS2;SOCS3;SOCS1;MYC;AKT2;IL21R;AKT1;JAK3;IL13RA1;SOCS5;IFNAR2;CISH;IFNGR1;IL10RB;STAT1;IFNGR2;IL10RA;STAT2;STAT3;OSM;OSMR;PRLR;GFAP;CSF2RB2;IL3RA;IL2RB;PTPN6;IRF9;CRLF2
+KEGG_2019_Mouse,Staphylococcus aureus infection,26/95,1.6688415080023846e-05,0.00010439136241546832,0,0,2.928800914659462,32.21914076309736,C1QB;C1QA;ITGAM;SELPLG;CFH;H2-DMA;ITGB2;C5AR1;PTAFR;ICAM1;C2;C4B;C3;H2-DMB1;C3AR1;H2-OB;H2-OA;H2-EB1;H2-AA;FCGR1;FCGR3;FCGR4;MASP1;FCGR2B;H2-AB1;C1QC
+KEGG_2019_Mouse,Cholesterol metabolism,17/49,1.7077989262616993e-05,0.00010460268423352907,0,0,4.121501865671642,45.24469387793783,ABCA1;MYLIP;LCAT;LPL;APOC3;LRP2;LIPA;CYP27A1;LIPC;SOAT1;NPC2;APOC2;APOC1;TSPO;VDAC1;APOE;LDLRAP1
+KEGG_2019_Mouse,Acute myeloid leukemia,21/69,1.85286354337813e-05,0.0001111718126026878,0,0,3.3970701407211963,37.01513243577278,CSF1R;CEBPA;TCF7L2;MAP2K1;SPI1;ITGAM;STAT3;PIK3R3;NFKB1;RUNX1;FCGR1;MYC;AKT2;EIF4EBP1;AKT1;MAPK1;KRAS;BCL2A1D;CD14;BCL2A1A;BCL2A1B
+KEGG_2019_Mouse,Platelet activation,31/125,2.359171103875394e-05,0.00013871926090787317,0,0,2.565319336891963,27.332490232441288,SNAP23;PIK3R3;ADCY2;PRKCZ;ADCY7;MYL12A;PIK3CG;GNAI1;PIK3R5;GNAI2;PTGS1;AKT2;PLCG2;AKT1;MAPK1;VASP;LYN;P2RY12;PRKCI;FCER1G;PLA2G4A;VAMP8;APBB1IP;GP9;MAPK11;TBXAS1;BTK;LCP2;TLN1;PLCB2;FERMT3
+KEGG_2019_Mouse,Glycosaminoglycan degradation,10/21,4.129539676638619e-05,0.00023805581665328507,0,0,7.039586234334593,71.06293017456677,HGSNAT;NAGLU;IDUA;GLB1;HEXB;HEXA;HYAL3;HPSE;GUSB;GNS
+KEGG_2019_Mouse,Sphingolipid signaling pathway,30/124,5.229293554651837e-05,0.0002956562125130077,0,0,2.481471044103142,24.463952701467036,ASAH1;TRADD;PIK3R3;TNF;PRKCZ;GNAI1;GNAI2;MAPK9;SGPL1;MAPK8;SPTLC2;PPP2R1A;ADORA3;AKT2;GNA12;BDKRB2;RAC2;AKT1;MAPK1;CTSD;MAP2K1;FCER1G;NFKB1;TNFRSF1A;MAPK11;PPP2R2C;PPP2R2B;KRAS;PLCB2;CERS2
+KEGG_2019_Mouse,Other glycan degradation,9/18,6.253277003109979e-05,0.00034687989413478,0,0,7.741032370953631,74.93179855716052,NEU4;GLB1;MAN2B2;FUCA1;HEXB;HEXA;FUCA2;MAN2B1;AGA
+KEGG_2019_Mouse,Hepatitis B,36/163,7.875760542371166e-05,0.00042879140730687457,0,0,2.205728268030241,20.84222573983821,PIK3R3;TNF;IFIH1;MAPK9;MAPK8;CASP8;CASP12;MYC;AKT2;AKT1;MAPK1;JAK3;MAP2K4;MAP2K1;EGR2;TICAM2;EGR3;STAT1;STAT2;STAT3;NFATC1;IRAK4;FOS;YWHAZ;NFKB1;TGFBR1;TGFBR2;MAPK11;MAVS;CDK2;IRF7;FAS;KRAS;TLR4;MYD88;TLR2
+KEGG_2019_Mouse,Autoimmune thyroid disease,21/78,0.00013559616189845982,0.0007248231199663125,0,0,2.859232514002685,25.463837234658577,CD86;H2-T23;H2-T22;H2-EB1;H2-DMA;H2-Q6;H2-K1;H2-Q7;H2-M3;H2-Q4;GZMB;H2-AA;TG;H2-DMB1;H2-OB;FAS;H2-OA;CGA;H2-D1;H2-Q10;H2-AB1
+KEGG_2019_Mouse,Intestinal immune network for IgA production,14/43,0.00020350292361323965,0.0010683903489695,0,0,3.741008934375426,31.79794066850137,CD86;H2-EB1;TNFRSF13B;H2-DMA;ICOSL;H2-AA;TNFSF13B;H2-DMB1;H2-OB;TNFRSF17;H2-OA;LTBR;MAP3K14;H2-AB1
+KEGG_2019_Mouse,Human papillomavirus infection,64/360,0.000241362238390062,0.0012449210190645,0,0,1.6871842707790714,14.052914949812196,H2-T23;H2-T22;MAML2;ITGB5;TRADD;H2-K1;TCIRG1;TNF;CASP8;PPP2R1A;AKT2;AKT1;OASL1;OASL2;IFNAR2;PRKCI;MAP2K1;TUBG2;TNFRSF1A;RBL1;COL4A4;MFNG;IRF1;COL4A3;COL6A1;ITGA5;ATP6V0D2;H2-D1;IRF9;ATP6V0E;PTGER4;HDAC2;H2-Q6;H2-M3;H2-Q7;H2-Q4;PIK3R3;PRKCZ;THBS1;THBS3;CHAD;SPP1;EIF4EBP1;MAPK1;TCF7L2;WNT10A;CSNK1A1;STAT1;STAT2;MX2;EIF2AK2;ISG15;PTK2;NFKB1;DLG1;CDK6;PPP2R2C;PPP2R2B;TADA3;CDK2;FAS;COL9A3;KRAS;H2-Q10
+KEGG_2019_Mouse,Viral myocarditis,22/87,0.00025497744642998763,0.0012924718836278,0,0,2.626687874378152,21.73409662680886,CD86;H2-T23;H2-T22;H2-EB1;CXADR;H2-DMA;H2-Q6;H2-K1;ITGB2;H2-Q7;H2-M3;H2-Q4;H2-AA;ICAM1;CASP8;H2-DMB1;H2-OB;RAC2;H2-OA;H2-D1;H2-Q10;H2-AB1
+KEGG_2019_Mouse,Fluid shear stress and atherosclerosis,31/143,0.0003380629576225311,0.0016845849074749,0,0,2.150835278899545,17.190074373061798,CCL12;PRKAA2;NCF1;SDC4;NCF2;PIK3R3;TNF;PRKCZ;ICAM1;MAPK9;MAPK8;CTSL;AKT2;RAC2;AKT1;HMOX1;CCL2;ACVR1;MAP2K4;HSP90AA1;IL1R1;DUSP1;CYBA;FOS;NFKB1;PTK2;TNFRSF1A;IL1A;MAPK11;TRPV4;NFE2L2
+KEGG_2019_Mouse,Glycosphingolipid biosynthesis,14/45,0.0003473463561669078,0.0017019971452178,0,0,3.499257541259493,27.872244635859552,B4GALT1;HEXB;HEXA;NAGA;GGTA1;FUT4;FUT9;GLB1;B3GALT1;ST3GAL5;ST8SIA5;ST3GAL6;ST6GALNAC5;ST6GALNAC6
+KEGG_2019_Mouse,Progesterone-mediated oocyte maturation,22/90,0.0004259112160011008,0.0020527524181036,0,0,2.5103775782200253,19.48374240301689,HSP90AA1;MAP2K1;PDE3B;PIK3R3;ADCY2;IGF1;ADCY7;GNAI1;GNAI2;STK10;RPS6KA3;CCNB2;MAPK9;MAPK11;MAPK8;AKT2;RPS6KA1;CDK2;CDK1;AKT1;MAPK1;KRAS
+KEGG_2019_Mouse,Leukocyte transendothelial migration,26/115,0.0005015861890562106,0.0023784893481052,0,0,2.268068396214736,17.23218286948132,ITGAM;NCF1;NCF2;NCF4;ITGB2;PIK3R3;THY1;F11R;MYL12A;GNAI1;ICAM1;GNAI2;PLCG2;RAC2;CTNNA3;VASP;CYBB;MSN;RHOH;CYBA;PTK2;VAV1;CLDN11;MAPK11;CLDN14;SIPA1
+KEGG_2019_Mouse,PI3K-Akt signaling pathway,62/357,0.0005639797264782736,0.0026319053902319,0,0,1.6386255474507958,12.257725713392846,CHRM2;CSF3R;CSF1;ITGB5;FGF1;FGF2;PIK3CG;GNGT2;PPP2R1A;MYC;AKT2;AKT1;JAK3;IFNAR2;MAP2K1;HSP90AA1;OSMR;PRLR;YWHAZ;COL4A4;COL4A3;COL6A1;IL3RA;ITGA5;TLR4;TLR2;CSF1R;PRKAA2;PKN3;IL4RA;PIK3R3;IL6RA;IL2RG;THBS1;PIK3R5;THBS3;GNG5;PDGFD;CHAD;SPP1;EIF4EBP1;MAPK1;ANGPT2;ANGPT1;BDNF;OSM;IGF1;PTK2;NFKB1;EFNA3;G6PC3;CDK6;LPAR5;PPP2R2C;LPAR6;PPP2R2B;GNB1;CDK2;IL2RB;COL9A3;KRAS;PIK3AP1
+KEGG_2019_Mouse,Legionellosis,16/58,0.0006136078694919558,0.0028187611504786,0,0,2.9525063206502438,21.83719285919761,RAB1A;ITGAM;ITGB2;TNF;NFKB1;NFKB2;PYCARD;C3;NAIP2;NAIP5;CASP8;NAIP6;CD14;TLR4;MYD88;TLR2
+KEGG_2019_Mouse,Necroptosis,35/176,0.0007988157492484908,0.0036001042599969,0,0,1.929140777003703,13.759365559009613,TRADD;TNF;PYCARD;MAPK9;FTL1;MAPK8;CASP8;FTH1;NLRP3;JAK3;CAMK2G;ZBP1;IFNAR2;IL33;HSP90AA1;PARP3;TICAM2;RIPK3;IFNGR1;STAT1;IFNGR2;STAT2;STAT3;CYBB;EIF2AK2;PLA2G4A;CFLAR;TNFRSF1A;SPATA2L;IL1A;FAS;VDAC1;TLR4;IRF9;BIRC3
+KEGG_2019_Mouse,Relaxin signaling pathway,28/131,0.00080818667061156,0.0036001042599969,0,0,2.1107232945469185,15.02986429721497,SHC1;PIK3R3;ADCY2;PRKCZ;ADCY7;RXFP2;GNAI1;GNAI2;MAPK9;GNA15;MAPK8;GNGT2;GNG5;AKT2;AKT1;MAPK1;MAP2K4;MAP2K1;FOS;NFKB1;TGFBR1;TGFBR2;MAPK11;COL4A4;COL4A3;GNB1;KRAS;PLCB2
+KEGG_2019_Mouse,Insulin signaling pathway,29/139,0.0009889710588372317,0.004339664049226,0,0,2.047079354890476,14.163425761900136,PRKAA2;SHC1;PDE3B;PIK3R3;PPP1R3A;SLC2A4;PRKCZ;HK2;SOCS2;SOCS3;HK3;MAPK9;MAPK8;SOCS1;PRKAR2B;AKT2;MKNK1;EIF4EBP1;AKT1;MAPK1;SH2B2;PRKCI;MAP2K1;PHKB;G6PC3;PRKAR1B;PPP1R3B;KRAS;FBP1
+KEGG_2019_Mouse,Cellular senescence,36/185,0.0010243834495922,0.004428951973237,0,0,1.877697264632744,12.925437739358008,H2-T23;H2-T22;TRAF3IP2;H2-Q6;H2-K1;H2-M3;H2-Q7;H2-Q4;PIK3R3;CCNB2;PPP3CA;PPP3R1;PPP3CC;CHEK2;MYC;AKT2;EIF4EBP1;AKT1;MAPK1;MAP2K1;NFATC1;NFKB1;TGFBR1;TGFBR2;IL1A;MAPK11;CDK6;RBL1;TRPV4;CDK2;CDK1;VDAC1;KRAS;H2-D1;H2-Q10;MCU
+KEGG_2019_Mouse,Asthma,9/25,0.001243237244396,0.0052972717369917,0,0,4.352608267716535,29.119108704881715,H2-EB1;FCER1G;H2-DMA;H2-DMB1;H2-OB;H2-OA;TNF;H2-AA;H2-AB1
+KEGG_2019_Mouse,Phospholipase D signaling pathway,30/149,0.0014841565404682,0.0062334574699666,0,0,1.9573710278813512,12.74817870718297,RALA;SHC1;PIK3R3;ADCY2;ADCY7;AGPAT2;PIK3CG;PIK3R5;GRM2;CYTH2;GRM4;CYTH4;AKT2;PDGFD;GRM8;GNA12;PLCG2;AKT1;MAPK1;MAP2K1;FCER1G;PLA2G4A;AGT;LPAR5;LPAR6;KRAS;AVP;PLPP2;PLCB2;PLPP1
+KEGG_2019_Mouse,Calcium signaling pathway,36/189,0.0015204342567552,0.0062958826969867,0,0,1.828190505950056,11.862688128812554,CHRM2;PDE1A;PTAFR;ADRA1D;ATP2A1;ADCY2;HTR2A;ADCY7;PPP3CA;GNA14;CYSLTR1;PPP3R1;GNA15;PPP3CC;CCKAR;GNA11;PLCG2;BDKRB2;PLCE1;CD38;CACNA1S;CAMK2G;PHKB;TPCN2;ITPKB;P2RX7;P2RX4;CCKBR;STIM2;ORAI3;VDAC1;PLCD3;PLCB2;PLCD4;MCU;CAMK1G
+KEGG_2019_Mouse,Proteoglycans in cancer,38/203,0.0015919807236426,0.006500587954874,0,0,1.789771888132544,11.53109990308394,CD63;SDC4;ITGB5;PIK3R3;HIF1A;TNF;FGF2;THBS1;PAK1;CTSL;PLAU;MYC;AKT2;PLCG2;AKT1;PLCE1;MAPK1;FLNC;CAMK2G;WNT10A;MAP2K1;STAT3;PLAUR;MSN;IGF1;PTK2;MAPK11;PDCD4;HCLS1;FAS;PTPN6;KRAS;HPSE;ITGA5;TLR4;CD44;TLR2;HBEGF
+KEGG_2019_Mouse,Primary immunodeficiency,11/36,0.0017332962758706,0.0069806726726845,0,0,3.405954465849387,21.65413998150865,PTPRC;TNFRSF13B;BLNK;TAP2;BTK;TAP1;IL2RG;CD3E;CD3D;JAK3;UNG
+KEGG_2019_Mouse,VEGF signaling pathway,15/58,0.001836789605335,0.0072975154590339,0,0,2.702264381884945,17.02355223772594,MAP2K1;PLA2G4A;PIK3R3;PTK2;PPP3CA;MAPK11;PPP3R1;PPP3CC;MAPKAPK3;AKT2;RAC2;PLCG2;AKT1;MAPK1;KRAS
+KEGG_2019_Mouse,Herpes simplex virus 1 infection,70/433,0.0019174991389802,0.0074588071143067,0,0,1.5030055405949174,9.403905077080108,H2-T23;H2-T22;TRADD;H2-K1;CGAS;TNF;IFIH1;CASP8;AKT2;H2-OB;AKT1;H2-OA;B2M;ZFP458;IFNAR2;ZFP455;ZFP1;IFNGR1;IFNGR2;TAP2;TAP1;IRAK4;TNFRSF1A;TAPBP;OAS2;OAS3;IRF7;SRSF4;ITGA5;H2-D1;IRF9;BIRC3;TLR2;CCL12;ZFP786;ZFP984;SP100;H2-DMA;H2-Q6;H2-M3;H2-Q7;H2-Q4;PIK3R3;OAS1A;ZFP949;OAS1B;OAS1G;C3;ZFP12;SOCS3;H2-DMB1;CCL5;EIF4EBP1;CCL2;CD74;H2-EB1;STAT1;STAT2;CARD9;EIF2AK2;PILRA;H2-AA;NFKB1;SRPK1;BST2;MAVS;FAS;MYD88;H2-Q10;H2-AB1
+KEGG_2019_Mouse,Central carbon metabolism in cancer,16/64,0.0019281270091405,0.0074588071143067,0,0,2.5825654526839257,16.14414919258778,MAP2K1;PDHA1;GLS2;PIK3R3;SLC1A5;HIF1A;HK2;GLS;HK3;MYC;AKT2;AKT1;MAPK1;KRAS;SLC16A3;PFKP
+KEGG_2019_Mouse,Glutamatergic synapse,24/114,0.0022785138864108,0.0086858489254324,0,0,2.068398649640393,12.584616948302928,HOMER1;GLS2;TRPC1;PLA2G4A;ADCY2;ADCY7;GNAI1;GLS;GNAI2;PPP3CA;GRM2;PPP3R1;GNGT2;PPP3CC;GRM4;GNG5;GNB1;GRM8;SLC17A6;MAPK1;DLGAP1;PLCB2;GRIA4;KCNJ3
+KEGG_2019_Mouse,Type II diabetes mellitus,13/48,0.00232292637272,0.0086858489254324,0,0,2.876048578940779,17.443026177023167,PRKCD;PIK3R3;SLC2A4;PRKCZ;TNF;HK2;SOCS2;HK3;SOCS3;MAPK9;MAPK8;SOCS1;MAPK1
+KEGG_2019_Mouse,Adipocytokine signaling pathway,17/71,0.0023339526024121,0.0086858489254324,0,0,2.439331122166943,14.78281508063989,PRKAA2;TRADD;STAT3;SLC2A4;TNFRSF1B;TNF;NFKB1;TNFRSF1A;CAMKK2;SOCS3;MAPK9;MAPK8;G6PC3;AKT2;NPY;AKT1;NFKBIE
+KEGG_2019_Mouse,Prion diseases,10/34,0.00378948395187,0.0139263535231223,0,0,3.224106491611962,17.97608773176908,C1QB;C1QA;EGR1;IL1A;MAP2K1;CASP12;C7;CCL5;MAPK1;C1QC
+KEGG_2019_Mouse,Cholinergic synapse,23/113,0.004333873483395,0.0157303556063967,0,0,1.981342918622848,10.781068478991209,CHRM2;MAP2K1;PIK3R3;ADCY2;FOS;ADCY7;PIK3CG;GNAI1;PIK3R5;GNAI2;GNGT2;GNG5;AKT2;GNA11;GNB1;AKT1;MAPK1;KRAS;CACNA1S;PLCB2;CAMK2G;KCNJ2;KCNJ3
+KEGG_2019_Mouse,Rap1 signaling pathway,37/209,0.0048298612407334,0.0173168195704346,0,0,1.6703426576307496,8.907833064138607,CSF1R;RALA;ITGAM;CSF1;ITGB2;PIK3R3;ADCY2;FGF1;PRKCZ;ADCY7;FGF2;THBS1;GNAI1;GNAI2;RASGRP3;AKT2;PDGFD;RAC2;AKT1;PLCE1;MAPK1;VASP;PRKCI;MAP2K1;ANGPT2;ANGPT1;IGF1;APBB1IP;MAPK11;EFNA3;LPAR5;LCP2;KRAS;TLN1;PLCB2;SIPA1;LAT
+KEGG_2019_Mouse,Transcriptional misregulation in cancer,33/183,0.0056310190438873,0.0199460192638901,0,0,1.7073828470380197,8.84332944299466,CD86;CSF1R;CEBPA;HDAC2;SPI1;ITGAM;HPGD;LYL1;HHEX;SIX4;PLAU;MYC;TSPAN7;CD14;BCL2A1D;BCL2A1A;BCL2A1B;GZMB;IGF1;NFKB1;FLI1;PBX1;PTK2;ETV5;RUNX1;TGFBR2;BAIAP3;FCGR1;NR4A3;SPINT1;IL2RB;BMP2K;BIRC3
+KEGG_2019_Mouse,GnRH signaling pathway,19/90,0.0060056159926726,0.0210196559743544,0,0,2.0733557761330728,10.605339712012428,MAP2K4;EGR1;MAP2K1;PRKCD;LHB;PLA2G4A;ADCY2;ADCY7;MAPK9;MAPK11;MAPK8;GNA11;MAPK1;KRAS;CACNA1S;CGA;PLCB2;CAMK2G;HBEGF
+KEGG_2019_Mouse,ErbB signaling pathway,18/84,0.0062742229929483,0.021701430116786,0,0,2.11270810875554,10.714188521104534,MAP2K4;MAP2K1;SHC1;PIK3R3;PTK2;MAPK9;PAK1;MAPK8;MYC;AKT2;NCK2;EIF4EBP1;PLCG2;AKT1;MAPK1;KRAS;CAMK2G;HBEGF
+KEGG_2019_Mouse,Primary bile acid biosynthesis,6/16,0.0065455986310726,0.0223768139248298,0,0,4.63826998689384,23.32568545756065,CYP27A1;HSD3B7;CH25H;ACOX2;CYP7B1;CYP8B1
+KEGG_2019_Mouse,Sphingolipid metabolism,12/48,0.0067998110244863,0.0229786717379195,0,0,2.579792670462841,12.87538522808488,UGCG;ASAH1;NEU4;SGPL1;UGT8A;SPTLC2;GLB1;ACER3;B4GALT6;PLPP2;PLPP1;CERS2
+KEGG_2019_Mouse,Viral carcinogenesis,39/229,0.0075402749181928,0.0251913730221442,0,0,1.5936100223964165,7.788763624081368,H2-T23;SP100;H2-T22;HDAC2;TRADD;H2-Q6;H2-K1;H2-M3;H2-Q7;H2-Q4;PIK3R3;HDAC9;CDC20;C3;CASP8;MAPK1;CCR5;JAK3;LYN;EGR2;EGR3;GSN;STAT3;EIF2AK2;YWHAZ;NFKB1;NFKB2;DLG1;CDK6;RBL1;CDK2;CDK1;IRF7;KRAS;LTBR;ATP6V0D2;H2-D1;H2-Q10;IRF9
+KEGG_2019_Mouse,Amino sugar and nucleotide sugar metabolism,12/49,0.0080856361289538,0.0260194184748854,0,0,2.5099264836452746,12.091987756102128,HK3;CYB5R1;GNPDA1;PMM1;HEXB;AMDHD2;HEXA;UAP1L1;UAP1;NPL;RENBP;HK2
+KEGG_2019_Mouse,Cytosolic DNA-sensing pathway,14/61,0.0081082526119702,0.0260194184748854,0,0,2.3059315156659546,11.102767152473932,ZBP1;IL33;RIPK3;TREX1;CGAS;NFKB1;PYCARD;CXCL10;MAVS;AIM2;CCL5;CCL4;IRF7;POLR3F
+KEGG_2019_Mouse,Long-term depression,14/61,0.0081082526119702,0.0260194184748854,0,0,2.3059315156659546,11.102767152473932,LYN;MAP2K1;PLA2G4A;IGF1;CRHR1;GNAI1;GNAI2;PPP2R1A;GNA11;GNA12;CRH;MAPK1;KRAS;PLCB2
+KEGG_2019_Mouse,Galactose metabolism,9/32,0.0081421309513246,0.0260194184748854,0,0,3.0267031838411502,14.560571132630884,HK3;B4GALT1;G6PC3;AKR1B10;GLB1;HK2;AKR1B8;GANC;PFKP
+KEGG_2019_Mouse,Oxytocin signaling pathway,28/154,0.0091044410344339,0.028781781334662,0,0,1.7231779640248983,8.097201119614452,PRKAA2;OXT;ADCY2;ADCY7;PIK3CG;GNAI1;PIK3R5;GNAI2;CAMKK2;PPP3CA;PPP3R1;PPP3CC;CD38;MAPK1;CACNA1S;CAMK2G;KCNJ2;KCNJ3;MAP2K1;CACNA2D1;PLA2G4A;NFATC1;FOS;CACNB3;CACNB4;KRAS;PLCB2;CAMK1G
+KEGG_2019_Mouse,Mineral absorption,11/44,0.0093711172762548,0.0293096646725417,0,0,2.5791009924109747,12.044718735314936,FTL1;SLC31A1;FTH1;TRF;HMOX1;CYBRD1;MT2;ATP1B3;MT1;ATP1B1;SLC39A4
+KEGG_2019_Mouse,Pancreatic cancer,16/75,0.0100549950617095,0.0311175636646589,0,0,2.099761269066867,9.658241981189049,MAP2K1;RALA;STAT1;STAT3;PIK3R3;TGFBR1;NFKB1;TGFBR2;MAPK9;MAPK8;CDK6;AKT2;RAC2;AKT1;MAPK1;KRAS
+KEGG_2019_Mouse,cGMP-PKG signaling pathway,30/172,0.0127042306347721,0.0389067063189897,0,0,1.6381867363119111,7.152028777758678,PDE3B;ADRA1D;ATP2A1;ADCY2;ADCY7;PIK3CG;GNAI1;PIK3R5;GNAI2;PPP3CA;PPP3R1;PPP3CC;ADORA3;AKT2;KCNMB1;GNA11;KCNMB2;GNA12;BDKRB2;AKT1;MAPK1;CACNA1S;VASP;MAP2K1;ATP1B3;NFATC1;ATP1B1;VDAC1;PDE5A;PLCB2
+KEGG_2019_Mouse,Insulin resistance,21/110,0.0129389342580637,0.0392169759986673,0,0,1.8278734695087604,7.946706145207282,PRKAA2;PRKCD;STAT3;PIK3R3;PPP1R3A;SLC2A4;TNF;PRKCZ;NFKB1;AGT;TNFRSF1A;RPS6KA3;SOCS3;MLXIPL;MAPK9;MAPK8;G6PC3;PPP1R3B;AKT2;RPS6KA1;AKT1
+KEGG_2019_Mouse,Fructose and mannose metabolism,9/35,0.0149657336028298,0.0448972008084894,0,0,2.677013930950938,11.248791436670365,HK3;PFKFB4;AKR1B10;PMM1;SORD;FBP1;HK2;AKR1B8;PFKP
+KEGG_2019_Mouse,Choline metabolism in cancer,19/99,0.016568833267943,0.0492044139472249,0,0,1.8391641036906852,7.540999258020067,SLC22A4;MAP2K1;CHKA;WAS;PIK3R3;PLA2G4A;FOS;HIF1A;MAPK9;MAPK8;AKT2;PDGFD;EIF4EBP1;RAC2;AKT1;MAPK1;KRAS;PLPP2;PLPP1
+KEGG_2019_Mouse,Neurotrophin signaling pathway,22/121,0.0191667527574073,0.0563502531067775,0,0,1.7212690032751623,6.806892753042416,MAP2K1;SHC1;BDNF;RIPK2;PRKCD;PIK3R3;IRAK4;NFKB1;RPS6KA3;MAPK9;MAPK11;MAPK8;AKT2;RPS6KA1;ARHGDIB;PLCG2;AKT1;MAPK1;KRAS;NFKBIE;CAMK2G;SH2B2
+KEGG_2019_Mouse,RIG-I-like receptor signaling pathway,14/68,0.0207845675475938,0.0602743437967694,0,0,2.0062188448860963,7.771177997441228,TRADD;ISG15;TNF;NFKB1;IFIH1;MAPK9;MAPK11;CXCL10;MAPK8;MAVS;CASP8;DHX58;IRF7;TRIM25
+KEGG_2019_Mouse,Malaria,11/49,0.0209115070315322,0.0602743437967694,0,0,2.2391118997142594,8.659666070197996,CCL12;CD81;ITGB2;CCL2;TNF;TLR4;THBS1;MYD88;ICAM1;TLR2;THBS3
+KEGG_2019_Mouse,Carbohydrate digestion and absorption,10/43,0.0213584606369215,0.0609649264782033,0,0,2.343611166368278,9.01424881970033,HK3;G6PC3;AKT2;PIK3R3;AKT1;ATP1B3;SLC2A5;ATP1B1;PLCB2;HK2
+KEGG_2019_Mouse,cAMP signaling pathway,34/211,0.0257912191046169,0.0729097924688211,0,0,1.489146595301814,5.446883056305248,CHRM2;PDE3B;PIK3R3;ADCY2;OXT;ADCY7;GNAI1;GNAI2;HCAR2;MAPK9;PAK1;MAPK8;AKT2;NPY;RAC2;AKT1;PLCE1;MAPK1;CACNA1S;CGA;CAMK2G;GRIA4;MAP2K1;BDNF;PDE4C;NFATC1;ATP1B3;FOS;ATP1B1;SSTR2;VAV1;NFKB1;ADCYAP1;FXYD1
+KEGG_2019_Mouse,Pathways in cancer,76/535,0.0288056547233528,0.0806558332253878,0,0,1.2868620283725127,4.564735839463324,SPI1;CSF3R;FGF1;FGF2;FRAT1;GNGT2;CASP8;MYC;AKT2;RAC2;BDKRB2;AKT1;JAK3;IL13RA1;IFNAR2;MAP2K1;HSP90AA1;IFNGR1;IFNGR2;FOS;TGFBR1;RUNX1;TGFBR2;CSF2RB2;MSH2;COL4A4;IL3RA;COL4A3;PLCB2;BIRC3;PTGER4;CSF1R;CEBPA;HDAC2;RALA;IL4RA;PIK3R3;ADCY2;IL6RA;CSF2RB;IL2RG;HIF1A;ADCY7;CSF2RA;GNAI1;GNAI2;RASGRP3;MAPK9;MAPK8;GNG5;GNA11;GNA12;PLCG2;HMOX1;MAPK1;CTNNA3;CAMK2G;TCF7L2;WNT10A;STAT1;STAT2;STAT3;IGF1;AGT;NFKB1;PTK2;NFKB2;CDK6;LPAR5;LPAR6;CDK2;IL2RB;GNB1;FAS;KRAS;NFE2L2
+KEGG_2019_Mouse,Adrenergic signaling in cardiomyocytes,25/148,0.0306408619819968,0.0849850322896892,0,0,1.5742630994591884,5.486969354571776,ADRA1D;ADCY2;ADCY7;PIK3CG;GNAI1;PIK3R5;GNAI2;PPP2R1A;AKT2;AKT1;MAPK1;CACNA1S;CAMK2G;TPM4;CACNA2D1;TPM1;ATP1B3;ATP1B1;AGT;MAPK11;CACNB3;CACNB4;PPP2R2C;PPP2R2B;PLCB2
+KEGG_2019_Mouse,Pentose and glucuronate interconversions,8/34,0.0352347749905469,0.0968133069833719,0,0,2.3785274629174937,7.957891085130448,AKR1B10;DCXR;SORD;UGT2A2;GUSB;UGT1A6A;UGT1A7C;AKR1B8
+KEGG_2019_Mouse,Ether lipid metabolism,10/47,0.0379356194538031,0.1032691862909085,0,0,2.089774676207937,6.837460163263971,UGT8A;LPCAT2;ENPP2;PLD4;PLA2G4A;GDPD3;PLA2G5;ENPP6;PLPP2;PLPP1
+KEGG_2019_Mouse,Ras signaling pathway,36/233,0.0390811296134678,0.1054114872143077,0,0,1.4163043258438328,4.591822274200834,CSF1R;RALA;CSF1;SHC1;PIK3R3;PLA2G5;FGF1;RASAL3;FGF2;RASGRP3;MAPK9;PAK1;MAPK8;GNGT2;GNG5;AKT2;PDGFD;PLCG2;RAC2;AKT1;PLCE1;MAPK1;BRAP;MAP2K1;ANGPT2;ANGPT1;BDNF;PLA2G4A;IGF1;NFKB1;EFNA3;RASA4;GNB1;KRAS;RGL2;LAT
+KEGG_2019_Mouse,Autophagy,22/130,0.0398834625710496,0.1065976181444416,0,0,1.5770233497906176,5.080843598058371,SH3GLB1;MAP2K1;PRKAA2;PRKCD;PIK3R3;CFLAR;HIF1A;CAMKK2;VAMP8;MAPK9;MAPK8;DEPTOR;CTSL;AKT2;LAMP2;ATG4C;AKT1;MAPK1;KRAS;RAB7B;CTSD;CTSB
+KEGG_2019_Mouse,Colorectal cancer,16/88,0.0413592774116506,0.1095461942254531,0,0,1.719370094095851,5.4769821146507365,TCF7L2;MAP2K1;RALA;PIK3R3;FOS;TGFBR1;TGFBR2;MAPK9;MAPK8;MSH2;MYC;AKT2;RAC2;AKT1;MAPK1;KRAS
+KEGG_2019_Mouse,FoxO signaling pathway,22/132,0.0461508572446835,0.1211460002672943,0,0,1.548174219093709,4.761935792212105,MAP2K1;PRKAA2;HOMER1;PLK2;STAT3;PIK3R3;IGF1;SLC2A4;NLK;SOD2;TGFBR1;TGFBR2;CCNB2;MAPK9;MAPK11;MAPK8;G6PC3;AKT2;CDK2;AKT1;MAPK1;KRAS
+KEGG_2019_Mouse,Chronic myeloid leukemia,14/76,0.0490806033022824,0.1263311093323549,0,0,1.7465599411689836,5.264640552600476,MAP2K1;HDAC2;SHC1;PIK3R3;TGFBR1;NFKB1;RUNX1;TGFBR2;CDK6;MYC;AKT2;AKT1;MAPK1;KRAS
+KEGG_2019_Mouse,Renin secretion,14/76,0.0490806033022824,0.1263311093323549,0,0,1.7465599411689836,5.264640552600476,PTGER4;PDE1A;PDE3B;GNAI1;AGT;GNAI2;PPP3CA;ADCYAP1;PPP3R1;PPP3CC;CACNA1S;PLCB2;KCNJ2;CTSB
+KEGG_2019_Mouse,GABAergic synapse,16/90,0.0494152298408871,0.1263311093323549,0,0,1.6727108855235229,5.030672310910125,GABRB3;GABRA6;GLS2;GABRA5;GAD1;GABRA3;ADCY2;ADCY7;GPHN;GNAI1;GLS;GNAI2;GNGT2;GNG5;GNB1;CACNA1S
+KEGG_2019_Mouse,Regulation of lipolysis in adipocytes,11/56,0.0510972541268849,0.1295051095974498,0,0,1.890056431212298,5.621074167778291,AKT2;NPY;PDE3B;AKT1;PIK3R3;ADCY2;CGA;ADCY7;GNAI1;GNAI2;PTGS1
+KEGG_2019_Mouse,IL-17 signaling pathway,16/91,0.0538303430658439,0.1352659902680182,0,0,1.650314465408805,4.822083498066425,HSP90AA1;CCL12;TRAF3IP2;TRADD;FOS;TNF;CXCL5;NFKB1;MAPK9;MAPK11;CXCL10;MAPK8;CASP8;CCL2;MAPK1;FOSB
+KEGG_2019_Mouse,Axon guidance,28/180,0.0583623367528678,0.1442007873863489,0,0,1.4263111461936713,4.0522705121819,EPHB6;SEMA3B;PIK3R3;SEMA3E;PRKCZ;MYL12A;GNAI1;GNAI2;PPP3CA;PPP3R1;PAK1;PPP3CC;ABLIM3;PLCG2;RAC2;NCK2;MAPK1;LRRC4C;CAMK2G;EPHB3;SEMA4D;TRPC1;SEMA4B;RHOD;PTK2;EFNA3;FES;KRAS
+KEGG_2019_Mouse,Steroid biosynthesis,5/19,0.058366985370665,0.1442007873863489,0,0,2.759045539613225,7.838461809065062,NSDHL;SOAT1;HSD17B7;LIPA;FDFT1
+KEGG_2019_Mouse,Thyroid hormone signaling pathway,19/115,0.0650514706147794,0.1593761030062096,0,0,1.5312454232571764,4.184245208528734,MAP2K1;HDAC2;STAT1;PIK3R3;ATP1B3;ATP1B1;HIF1A;MYC;AKT2;RCAN2;PLCG2;AKT1;PLCE1;MAPK1;KRAS;PLCD3;PLCB2;PLCD4;PFKP
+KEGG_2019_Mouse,Apelin signaling pathway,22/138,0.0691751323176173,0.1680784206725578,0,0,1.4675956126644114,3.920114953267941,EGR1;MAP2K1;PRKAA2;PDE3B;ADCY2;ADCY7;PIK3CG;TGFBR1;GNAI1;PIK3R5;GNAI2;APLN;GNGT2;GNG5;AKT2;GNB1;SPP1;AKT1;MAPK1;KRAS;CCN2;PLCB2
+KEGG_2019_Mouse,Folate biosynthesis,6/26,0.0697565200623415,0.168101777855151,0,0,2.3178243774574048,6.171773846613959,AKR1B10;GCH1;TH;MOCOS;GPHN;AKR1B8
+KEGG_2019_Mouse,Focal adhesion,30/199,0.0720302940273752,0.1721699710898238,0,0,1.374348524628708,3.615455369118644,ITGB5;SHC1;PIK3R3;THBS1;MYL12A;THBS3;MAPK9;PAK1;MAPK8;AKT2;PDGFD;CHAD;RAC2;SPP1;AKT1;MAPK1;FLNC;VASP;MAP2K1;IGF1;PTK2;VAV1;PARVG;COL4A4;COL4A3;COL6A1;COL9A3;ITGA5;TLN1;BIRC3
+KEGG_2019_Mouse,Serotonergic synapse,21/132,0.0757522267486256,0.1796060860007737,0,0,1.46376191494925,3.776926482834897,GABRB3;MAP2K1;DUSP1;TRPC1;PLA2G4A;HTR3A;HTR2A;GNAI1;GNAI2;PTGS1;CYP4X1;GNGT2;GNG5;ALOX5;GNB1;MAPK1;KCNN2;KRAS;CACNA1S;PLCB2;KCNJ3
+KEGG_2019_Mouse,ECM-receptor interaction,14/83,0.0897169242083849,0.2110142057381214,0,0,1.568750039710526,3.7824067136267976,ITGB5;SDC4;THBS1;THBS3;GP9;SV2B;COL4A4;COL4A3;COL6A1;CHAD;SPP1;COL9A3;ITGA5;CD44
+KEGG_2019_Mouse,Dopaminergic synapse,21/135,0.091122231961731,0.212618541244039,0,0,1.4249988427533211,3.4136609159372853,FOS;GNAI1;GNAI2;PPP3CA;MAPK9;MAPK11;MAPK8;GNGT2;PPP3CC;TH;GNG5;PPP2R2C;PPP2R1A;PPP2R2B;AKT2;GNB1;AKT1;PLCB2;CAMK2G;GRIA4;KCNJ3
+KEGG_2019_Mouse,Mucin type O-glycan biosynthesis,6/28,0.0939706066870495,0.2158387372343169,0,0,2.1068747765995472,4.9822810926394885,GALNT11;GALNT6;GALNT4;GALNT15;GCNT4;GALNTL6
+KEGG_2019_Mouse,Protein export,6/28,0.0939706066870495,0.2158387372343169,0,0,2.1068747765995472,4.9822810926394885,SEC61A2;SRP54B;SRP54C;SRP54A;SEC62;SEC63
+KEGG_2019_Mouse,TGF-beta signaling pathway,15/91,0.0947081509093136,0.215846483467738,0,0,1.5260560941828254,3.596845864448201,ACVR1;TGIF1;LEFTY1;FST;TNF;THBS1;TGFBR1;TGFBR2;RBL1;TFDP1;ACVR1C;PPP2R1A;MYC;MAPK1;ID3
+KEGG_2019_Mouse,Amoebiasis,17/106,0.0964216167901285,0.2180611948945983,0,0,1.4771088378333057,3.4549942954867907,ITGAM;IL1R1;ITGB2;PIK3R3;TNF;NFKB1;PTK2;GNA14;GNA15;COL4A4;GNA11;COL4A3;CD14;RAB7B;TLR4;PLCB2;TLR2
+KEGG_2019_Mouse,Neuroactive ligand-receptor interaction,48/348,0.1016657785793736,0.2246100058267063,0,0,1.2393413440142411,2.833214282609516,GABRB3;PTGER4;CHRM2;LHB;PTAFR;PMCH;NPY2R;C5AR1;OXT;ADRA1D;P2RY10B;HTR2A;RXFP2;CRHR1;C3;GRM2;CYSLTR1;P2RY6;CCKAR;GRM4;CNR2;NPY;ADORA3;GRM8;C3AR1;BDKRB2;TSPO;CGA;HCRT;NTSR2;GRIA4;P2RY13;CHRNB3;GABRA6;GABRA5;GABRA3;SCTR;SSTR2;PRLR;AGT;APLN;P2RX7;ADCYAP1;P2RX4;CCKBR;LPAR6;CRH;AVP
+KEGG_2019_Mouse,Morphine addiction,15/92,0.1017594080797915,0.2246100058267063,0,0,1.5061517429938482,3.441773612918548,GABRB3;GABRA6;GABRA5;PDE1A;PDE3B;PDE4C;GABRA3;ADCY2;ADCY7;GNAI1;GNAI2;GNGT2;GNG5;GNB1;KCNJ3
+KEGG_2019_Mouse,"Parathyroid hormone synthesis, secretion and action",17/107,0.1029924812143753,0.2246100058267063,0,0,1.4606135986733002,3.3201197360595143,EGR1;MAP2K1;PDE4C;ADCY2;GATA3;FOS;ADCY7;GNAI1;GNAI2;NR4A2;MAFB;MMP24;GNA11;GNA12;MAPK1;PLCB2;HBEGF
+KEGG_2019_Mouse,Endocytosis,38/269,0.1030264382902637,0.2246100058267063,0,0,1.2735980604833064,2.894595007034147,H2-T23;H2-T22;WIPF1;ARPC1B;H2-Q6;H2-K1;H2-M3;H2-Q7;WAS;ASAP3;H2-Q4;VPS26B;IL2RG;PRKCZ;CYTH2;CYTH4;PSD4;CCR5;LDLRAP1;SH3GL2;PSD;SH3GLB1;SH3GLB2;PRKCI;ARAP1;TGFBR1;TGFBR2;EPN3;ACAP3;DAB2;EHD4;DNAJC6;IL2RB;AMPH;FOLR2;SMAP1;H2-D1;H2-Q10
+KEGG_2019_Mouse,Wnt signaling pathway,24/160,0.1031372475734876,0.2246100058267063,0,0,1.3652187427150515,3.1013601508077118,TCF7L2;WNT10A;CSNK1A1;NFATC1;PRICKLE1;NLK;DKK2;PPP3CA;MAPK9;FRAT1;PPP3R1;MAPK8;PPP3CC;MYC;SFRP5;RAC2;RSPO2;CCN4;RSPO3;LGR6;PLCB2;CAMK2G;LGR5;LGR4
+KEGG_2019_Mouse,"Neomycin, kanamycin and gentamicin biosynthesis",2/5,0.103955336518724,0.2247269774743004,0,0,5.146678296263992,11.651019079716708,HK3;HK2
+KEGG_2019_Mouse,Fatty acid elongation,6/29,0.1075621083412231,0.230826714250508,0,0,2.015157558835261,4.493170302031011,ELOVL1;ELOVL4;ELOVL2;ELOVL7;HACD2;HACD4
+KEGG_2019_Mouse,Gap junction,14/86,0.1121611579897936,0.2372329528704988,0,0,1.5031297189341906,3.2885750540376573,MAP2K1;ADCY2;HTR2A;ADCY7;GNAI1;GNAI2;GJD2;TUBA1C;GNA11;PDGFD;CDK1;MAPK1;KRAS;PLCB2
+KEGG_2019_Mouse,Insulin secretion,14/86,0.1121611579897936,0.2372329528704988,0,0,1.5031297189341906,3.2885750540376573,ATP1B3;ADCY2;ATP1B1;ADCY7;ADCYAP1;CCKAR;GNA11;KCNMB1;KCNMB2;KCNN2;CACNA1S;PLCB2;CAMK2G;VAMP2
+KEGG_2019_Mouse,Oocyte meiosis,18/116,0.1131589428044854,0.2376337798894193,0,0,1.420262966846818,3.0946988573424457,MAP2K1;ADCY2;IGF1;YWHAZ;ADCY7;CDC20;RPS6KA3;CCNB2;PPP3CA;PPP3R1;PPP3CC;STAG3;PPP2R1A;RPS6KA1;CDK2;CDK1;MAPK1;CAMK2G
+KEGG_2019_Mouse,Histidine metabolism,5/24,0.1330412184922512,0.2774050938774601,0,0,2.0324063433693405,4.099559285426533,CARNMT1;AMDHD1;HDC;ALDH3B1;CNDP2
+KEGG_2019_Mouse,Amyotrophic lateral sclerosis (ALS),9/52,0.1361180424303016,0.2818218624965399,0,0,1.6171030946713056,3.22488004961073,PPP3CA;MAPK11;PPP3R1;PPP3CC;CASP12;DERL1;TNFRSF1B;TNF;TNFRSF1A
+KEGG_2019_Mouse,Glycolysis / Gluconeogenesis,11/67,0.140921523619915,0.2892619570544918,0,0,1.51784932449337,2.9743048519648028,HK3;MINPP1;ACSS2;PDHA1;G6PC3;ALDH3B1;DLAT;ENO2;FBP1;HK2;PFKP
+KEGG_2019_Mouse,Prostate cancer,15/97,0.1416793259042409,0.2892619570544918,0,0,1.4139120667522465,2.763051469169924,TCF7L2;MAP2K1;HSP90AA1;PIK3R3;IGF1;NFKB1;ETV5;SPINT1;PLAU;AKT2;PDGFD;CDK2;AKT1;MAPK1;KRAS
+KEGG_2019_Mouse,Pantothenate and CoA biosynthesis,4/18,0.1436224563902974,0.2907910820676282,0,0,2.206272993702064,4.281421122858652,PANK2;BCAT1;PPCDC;COASY
+KEGG_2019_Mouse,Hippo signaling pathway,23/159,0.1444064557206589,0.2907910820676282,0,0,1.3077587769262635,2.5306745405796303,YAP1;TCF7L2;PRKCI;WNT10A;ITGB2;AFP;FGF1;PRKCZ;YWHAZ;TGFBR1;TGFBR2;MOB1A;DLG1;PAK1;FRMD6;PPP2R2C;PPP2R1A;RASSF4;PPP2R2B;MYC;SNAI2;CTNNA3;CCN2
+KEGG_2019_Mouse,Glioma,12/75,0.1470044961561829,0.2940089923123658,0,0,1.471914565212857,2.8220901774348883,MAP2K1;CDK6;SHC1;AKT2;PLCG2;AKT1;PIK3R3;MAPK1;KRAS;IGF1;CAMK2G;CAMK1G
+KEGG_2019_Mouse,Biosynthesis of unsaturated fatty acids,6/32,0.1538600291511363,0.3056408687191492,0,0,1.78233692912592,3.3360214031133744,ELOVL1;ELOVL4;ELOVL2;ELOVL7;HACD2;HACD4
+KEGG_2019_Mouse,Circadian entrainment,15/99,0.1597674471696524,0.3152458353548846,0,0,1.380090852130326,2.531136273270981,ADCY2;FOS;ADCY7;GNAI1;GNAI2;ADCYAP1;GNGT2;RASD1;GNG5;GNB1;MAPK1;PLCB2;CAMK2G;GRIA4;KCNJ3
+KEGG_2019_Mouse,Regulation of actin cytoskeleton,30/217,0.1618792786685702,0.3172833861903976,0,0,1.2407833693381023,2.2593479152221296,CHRM2;NCKAP1;ITGAM;ITGB5;ARPC1B;ITGB2;WAS;PIK3R3;FGF1;IQGAP3;FGF2;MYL12A;PAK1;PDGFD;GNA12;RAC2;BDKRB2;ITGAX;MAPK1;NCKAP1L;MAP2K1;GSN;MSN;BAIAP2;PTK2;VAV1;LPAR5;SPATA13;KRAS;ITGA5
+KEGG_2019_Mouse,Fat digestion and absorption,7/40,0.1686728428727597,0.326248788188101,0,0,1.6383767747404112,2.915973614151105,ABCA1;NPC1L1;PLA2G5;AGPAT2;PLPP2;ACAT2;PLPP1
+KEGG_2019_Mouse,Ferroptosis,7/40,0.1686728428727597,0.326248788188101,0,0,1.6383767747404112,2.915973614151105,FTL1;FTH1;TRF;HMOX1;CYBB;SLC7A11;SAT1
+KEGG_2019_Mouse,SNARE interactions in vesicular transport,6/33,0.1709727748121537,0.3285359202272759,0,0,1.7162273676035145,3.0312882129191543,VAMP8;SNAP23;STX6;STX3;VAMP5;VAMP2
+KEGG_2019_Mouse,Salivary secretion,12/78,0.179456840250486,0.3425994222963825,0,0,1.4047704376219488,2.413143517705069,CST3;SLC12A2;LYZ2;CD38;ADRA1D;ATP1B3;ADCY2;LPO;ATP1B1;PLCB2;ADCY7;VAMP2
+KEGG_2019_Mouse,p53 signaling pathway,11/71,0.1866341802342055,0.3540028967022995,0,0,1.4163382953882078,2.3774723085472145,CCNB2;CASP8;CDK6;CD82;CHEK2;CDK2;SHISA5;CDK1;FAS;IGF1;THBS1
+KEGG_2019_Mouse,Glucagon signaling pathway,15/102,0.1890351456088616,0.35625854364747,0,0,1.3322746521476103,2.219332859388658,PDHA1;PRKAA2;PDE3B;PHKB;ADCY2;PPP3CA;PPP3R1;G6PC3;PPP3CC;AKT2;AKT1;SIK1;FBP1;PLCB2;CAMK2G
+KEGG_2019_Mouse,Estrogen signaling pathway,19/134,0.1949405252754114,0.3650478626176495,0,0,1.2768778176816689,2.08777282206374,MAP2K1;HSP90AA1;SHC1;PRKCD;PIK3R3;ADCY2;FOS;ADCY7;GNAI1;GNAI2;KRT18;AKT2;AKT1;MAPK1;KRAS;CTSD;PLCB2;HBEGF;KCNJ3
+KEGG_2019_Mouse,Adherens junction,11/72,0.1990193273965197,0.367998001601112,0,0,1.3930406821509576,2.248859872940909,TCF7L2;RAC2;WAS;MAPK1;SNAI2;CTNNA3;PTPN6;NLK;BAIAP2;TGFBR1;TGFBR2
+KEGG_2019_Mouse,Melanoma,11/72,0.1990193273965197,0.367998001601112,0,0,1.3930406821509576,2.248859872940909,MAP2K1;CDK6;AKT2;PDGFD;AKT1;PIK3R3;MAPK1;KRAS;IGF1;FGF1;FGF2
+KEGG_2019_Mouse,Inflammatory mediator regulation of TRP channels,18/127,0.2033092446517827,0.3735807370476507,0,0,1.276135910360083,2.0329190711652103,PTGER4;IL1R1;PRKCD;PIK3R3;PLA2G4A;ADCY2;IGF1;HTR2A;ADCY7;MAPK9;MAPK11;MAPK8;TRPV4;PLCG2;BDKRB2;ASIC2;PLCB2;CAMK2G
+KEGG_2019_Mouse,Tight junction,23/167,0.2048488925494681,0.374071890742507,0,0,1.2345431093505477,1.9573467190173868,VASP;ACTR2;PRKCI;PRKAA2;MYH15;WAS;MSN;F11R;PRKCZ;MYL12A;RUNX1;CLDN11;TUBA1C;MAPK9;DLG1;MAPK8;PPP2R2C;PPP2R1A;CLDN14;PPP2R2B;MICALL2;HCLS1;TJP3
+KEGG_2019_Mouse,Arginine and proline metabolism,8/50,0.2099930428847757,0.3810984852353338,0,0,1.4710891790034772,2.2959007514057146,OAT;P4HA2;AZIN2;HOGA1;LAP3;SAT1;CNDP2;SRM
+KEGG_2019_Mouse,Inositol phosphate metabolism,11/73,0.2117536352850311,0.381936004747234,0,0,1.370494604824586,2.1274623263471275,ITPKB;MINPP1;MTMR1;INPP5D;PLCG2;INPP5J;PLCE1;PLCD3;PLCB2;PLCD4;PIK3CG
+KEGG_2019_Mouse,Endometrial cancer,9/58,0.2164580248277253,0.3880406054838491,0,0,1.4186083882371847,2.170979591004849,TCF7L2;MAP2K1;MYC;AKT2;AKT1;PIK3R3;MAPK1;CTNNA3;KRAS
+KEGG_2019_Mouse,Glycerophospholipid metabolism,14/97,0.2193124510528271,0.3907749127850375,0,0,1.3031063315075295,1.977148309331174,CDS1;PCYT2;CHKA;PNPLA7;PLD4;PLA2G4A;LCAT;PLA2G5;AGPAT2;PLA2G15;GPD1;LPCAT2;PLPP2;PLPP1
+KEGG_2019_Mouse,Non-small cell lung cancer,10/66,0.2212506356270209,0.3918535353876153,0,0,1.3792591434823382,2.08055603687436,MAP2K1;CDK6;AKT2;STAT3;PLCG2;AKT1;PIK3R3;MAPK1;KRAS;JAK3
+KEGG_2019_Mouse,Gastric acid secretion,11/74,0.224817997101676,0.3957873721430703,0,0,1.3486642759847665,2.012833022797478,CCKBR;ATP1B3;ADCY2;SSTR2;ATP1B1;PLCB2;ADCY7;CAMK2G;GNAI1;KCNJ2;GNAI2
+KEGG_2019_Mouse,Dilated cardiomyopathy (DCM),13/90,0.2293946680372743,0.40144066906523,0,0,1.304187486483718,1.920169996671506,ITGB5;TPM4;CACNA2D1;TPM1;ADCY2;IGF1;TNF;ADCY7;AGT;CACNB3;CACNB4;ITGA5;CACNA1S
+KEGG_2019_Mouse,Long-term potentiation,10/67,0.2353434623717141,0.4094140706348163,0,0,1.3549848362701062,1.960269150229272,PPP3CA;RPS6KA3;MAP2K1;PPP3R1;PPP3CC;RPS6KA1;MAPK1;KRAS;PLCB2;CAMK2G
+KEGG_2019_Mouse,Other types of O-glycan biosynthesis,4/22,0.2408694042238993,0.414126344104248,0,0,1.715602114554537,2.442160268095662,ST6GAL1;B4GALT1;MFNG;COLGALT1
+KEGG_2019_Mouse,Proximal tubule bicarbonate reclamation,4/22,0.2408694042238993,0.414126344104248,0,0,1.715602114554537,2.442160268095662,GLS2;ATP1B3;ATP1B1;GLS
+KEGG_2019_Mouse,Aldosterone-regulated sodium reabsorption,6/38,0.2663428882077572,0.4515257518109321,0,0,1.447657273918742,1.9152082249370947,PIK3R3;MAPK1;ATP1B3;KRAS;IGF1;ATP1B1
+KEGG_2019_Mouse,Mannose type O-glycan biosynthesis,4/23,0.2672295265819802,0.4515257518109321,0,0,1.62521537365894,2.144711144369257,B4GALT1;FKRP;FUT9;FUT4
+KEGG_2019_Mouse,Terpenoid backbone biosynthesis,4/23,0.2672295265819802,0.4515257518109321,0,0,1.62521537365894,2.144711144369257,FDPS;MVK;MVD;ACAT2
+KEGG_2019_Mouse,Retrograde endocannabinoid signaling,20/150,0.2711276021321837,0.4554943715820686,0,0,1.1885038038884193,1.5511944138259215,GABRB3;GABRA6;GABRA5;GABRA3;ADCY2;ADCY7;GNAI1;GNAI2;MAPK9;MAPK11;MAPK8;GNGT2;GNG5;GNB1;SLC17A6;MAPK1;CACNA1S;PLCB2;GRIA4;KCNJ3
+KEGG_2019_Mouse,AMPK signaling pathway,17/126,0.2755115572015537,0.4598186135908845,0,0,1.2047103929891825,1.5530231302908082,PFKFB4;PRKAA2;CAB39;PIK3R3;IGF1;SLC2A4;CAMKK2;G6PC3;PPP2R2C;RAB14;PPP2R1A;PPP2R2B;AKT2;EIF4EBP1;AKT1;FBP1;PFKP
+KEGG_2019_Mouse,Longevity regulating pathway,14/102,0.2788372403666053,0.4598186135908845,0,0,1.2287174684149695,1.569228297593793,HDAC2;PRKAA2;PIK3R3;ADCY2;IGF1;SOD2;ADCY7;NFKB1;CAMKK2;AKT2;EIF4EBP1;AKT1;KRAS;CRYAB
+KEGG_2019_Mouse,Hypertrophic cardiomyopathy (HCM),12/86,0.279948407212606,0.4598186135908845,0,0,1.2523351209290765,1.5944104000327806,CACNB3;CACNB4;PRKAA2;ITGB5;TPM4;CACNA2D1;TPM1;ITGA5;CACNA1S;IGF1;TNF;AGT
+KEGG_2019_Mouse,Cardiac muscle contraction,11/78,0.2799575912679194,0.4598186135908845,0,0,1.267859476697075,1.614133639822116,CACNB3;CACNB4;TPM4;SLC9A6;CACNA2D1;TPM1;ATP1B3;CACNA1S;ATP1B1;COX6A2;COX7A1
+KEGG_2019_Mouse,Citrate cycle (TCA cycle),5/32,0.303077307567454,0.4927728589721247,0,0,1.4295649361151546,1.7065679676387069,PDHA1;DLST;SUCLG2;DLAT;ACO2
+KEGG_2019_Mouse,Nicotine addiction,6/40,0.3077459811253703,0.4927728589721247,0,0,1.362346773571814,1.6054992062030813,GABRB3;GABRA6;GABRA5;GABRA3;SLC17A6;GRIA4
+KEGG_2019_Mouse,Nitrogen metabolism,3/17,0.3087937646202704,0.4927728589721247,0,0,1.6539827973074046,1.94356484003482,CAR14;CAR7;CAR5B
+KEGG_2019_Mouse,ABC transporters,7/48,0.3100277570501171,0.4927728589721247,0,0,1.3180965376087328,1.543614217232415,ABCA1;ABCC3;ABCA4;TAP2;ABCA9;TAP1;ABCB1B
+KEGG_2019_Mouse,Cocaine addiction,7/48,0.3100277570501171,0.4927728589721247,0,0,1.3180965376087328,1.543614217232415,GRM2;TH;BDNF;FOSB;GNAI1;NFKB1;GNAI2
+KEGG_2019_Mouse,Bile secretion,10/72,0.3100781595572894,0.4927728589721247,0,0,1.2453589327309946,1.458229238089225,ABCC3;EPHX1;SCTR;AQP4;ATP1B3;ADCY2;KCNN2;ATP1B1;ADCY7;ABCB1B
+KEGG_2019_Mouse,Arachidonic acid metabolism,12/89,0.3215311968456827,0.5082267304980146,0,0,1.2033380548492243,1.365380405334316,CBR2;HPGDS;CYP4F18;ALOX5;TBXAS1;PLA2G4A;CYP2E1;PLA2G5;LTC4S;CYP2B19;PTGES;PTGS1
+KEGG_2019_Mouse,Ovarian steroidogenesis,8/57,0.328655618628304,0.514270126363934,0,0,1.2604338630948664,1.4025412626155844,ALOX5;LHB;PLA2G4A;ADCY2;IGF1;HSD17B7;CGA;ADCY7
+KEGG_2019_Mouse,Bladder cancer,6/41,0.3288530059742163,0.514270126363934,0,0,1.3233476876989327,1.4717537444891424,MAP2K1;MYC;MAPK1;KRAS;THBS1;HBEGF
+KEGG_2019_Mouse,Phosphatidylinositol signaling system,13/98,0.332757783999787,0.5176232195552243,0,0,1.1809042635459093,1.2993967040094343,CDS1;MTMR1;PIK3R3;ITPKB;PPIP5K1;INPP5D;PLCG2;INPP5J;PLCE1;PLCD3;PLCB2;PLCD4;IP6K3
+KEGG_2019_Mouse,Bacterial invasion of epithelial cells,10/74,0.3414457261780551,0.5283423341913064,0,0,1.206304704595186,1.296254673010228,SHC1;ARPC1B;RHOG;WAS;HCLS1;PIK3R3;ELMO3;CTNNA3;ITGA5;PTK2
+KEGG_2019_Mouse,Metabolism of xenobiotics by cytochrome P450,9/66,0.3441406462628638,0.52972434555645,0,0,1.218952894046139,1.300262963698298,CBR2;HPGDS;ALDH3B1;EPHX1;UGT2A2;CYP2E1;CYP2F2;UGT1A6A;UGT1A7C
+KEGG_2019_Mouse,Gastric cancer,19/150,0.3594784416339217,0.5504513637519427,0,0,1.119903674586458,1.1457746491749192,TCF7L2;MAP2K1;WNT10A;SHC1;CSNK1A1;PIK3R3;FGF1;FGF2;TGFBR1;TGFBR2;FRAT1;MYC;AKT2;CDK2;AKT1;MAPK1;CTNNA3;KRAS;ABCB1B
+KEGG_2019_Mouse,Synthesis and degradation of ketone bodies,2/11,0.3652797003696404,0.5522152626365774,0,0,1.7149779522217377,1.7271404326184991,BDH1;ACAT2
+KEGG_2019_Mouse,Taurine and hypotaurine metabolism,2/11,0.3652797003696404,0.5522152626365774,0,0,1.7149779522217377,1.7271404326184991,GGT7;GAD1
+KEGG_2019_Mouse,Cushing syndrome,20/159,0.366265225218138,0.5522152626365774,0,0,1.1109811052257097,1.1158666990222672,TCF7L2;MAP2K1;WNT10A;ADCY2;ADCY7;CRHR1;PBX1;GNAI1;AGT;GNAI2;CDK6;RASD1;GNA11;CDK2;CRH;MAPK1;CACNA1S;PLCB2;CAMK2G;KCNK3
+KEGG_2019_Mouse,Non-alcoholic fatty liver disease (NAFLD),19/151,0.3707374567762177,0.552540250477765,0,0,1.1113563135751183,1.1027556721661491,CEBPA;PRKAA2;PIK3R3;IL6RA;TNF;COX6A2;COX7A1;NFKB1;TNFRSF1A;SOCS3;MLXIPL;IL1A;MAPK9;MAPK8;CASP8;AKT2;AKT1;FAS;CYP2E1
+KEGG_2019_Mouse,Vasopressin-regulated water reabsorption,6/43,0.3715216401628191,0.552540250477765,0,0,1.2516736920406646,1.239342410286694,ARHGDIB;AQP4;AVP;AQP3;DYNC1I1;VAMP2
+KEGG_2019_Mouse,One carbon pool by folate,3/19,0.3747287810926333,0.552540250477765,0,0,1.447071335078534,1.4203768702478166,MTHFD2;TYMS;ALDH1L2
+KEGG_2019_Mouse,Systemic lupus erythematosus,18/143,0.3753783274612537,0.552540250477765,0,0,1.1117786561264822,1.0893439504969613,CD86;C1QB;C1QA;H2-EB1;H2-DMA;TNF;H2-AA;C2;C4B;FCGR1;C3;C7;FCGR4;H2-DMB1;H2-OB;H2-OA;H2-AB1;C1QC
+KEGG_2019_Mouse,Butanoate metabolism,4/27,0.3758777214134456,0.552540250477765,0,0,1.3422655760727231,1.3133953193353978,BDH1;GAD1;ACADS;ACAT2
+KEGG_2019_Mouse,Glycosaminoglycan biosynthesis,7/53,0.4074454495077206,0.5959649858471138,0,0,1.1744926269382792,1.0545161162623675,B4GALT1;UST;CHST1;CHSY3;CHST14;HS6ST2;HS3ST2
+KEGG_2019_Mouse,Thyroid cancer,5/37,0.4218479541407008,0.6115801540972894,0,0,1.2058542576419211,1.040785263970883,TCF7L2;MAP2K1;MYC;MAPK1;KRAS
+KEGG_2019_Mouse,Breast cancer,18/147,0.4222815349719379,0.6115801540972894,0,0,1.0770597787786869,0.9285149714799144,TCF7L2;MAP2K1;WNT10A;SHC1;CSNK1A1;PIK3R3;IGF1;FOS;FGF1;FGF2;NFKB2;FRAT1;CDK6;MYC;AKT2;AKT1;MAPK1;KRAS
+KEGG_2019_Mouse,Pancreatic secretion,13/105,0.4297962676907519,0.6194122681425542,0,0,1.0906194032694432,0.9209669880781446,SLC12A2;TRPC1;SCTR;ATP1B3;ATP2A1;ADCY2;PLA2G5;ATP1B1;ADCY7;TPCN2;CCKAR;CD38;PLCB2
+KEGG_2019_Mouse,Vascular smooth muscle contraction,17/140,0.440748646275792,0.6320980585613798,0,0,1.066739473364883,0.8739588801709768,PPP1R14A;MAP2K1;PRKCD;PLA2G4A;ADRA1D;ADCY2;PLA2G5;ADCY7;AGT;KCNMB1;GNA11;KCNMB2;GNA12;MAPK1;CACNA1S;AVP;PLCB2
+KEGG_2019_Mouse,Pyruvate metabolism,5/38,0.4454093192810239,0.6356812614981603,0,0,1.1692470557099377,0.94564212071801,ACSS2;PDHA1;ME3;DLAT;ACAT2
+KEGG_2019_Mouse,Protein digestion and absorption,11/90,0.4603342518598016,0.6538080678588487,0,0,1.074541665742978,0.8336320234838364,SLC7A7;MME;COL4A4;COL5A3;COL4A3;COL6A1;ATP1B3;PRCP;COL9A3;SLC1A5;ATP1B1
+KEGG_2019_Mouse,Thyroid hormone synthesis,9/73,0.4630691542775475,0.6545304392192258,0,0,1.085199311023622,0.835472024459664,TG;IYD;ATP1B3;ADCY2;LRP2;ATP1B1;CGA;PLCB2;ADCY7
+KEGG_2019_Mouse,African trypanosomiasis,5/39,0.468721707991271,0.6593501538250416,0,0,1.1347932185974827,0.8598850900849035,FAS;TNF;PLCB2;MYD88;ICAM1
+KEGG_2019_Mouse,Retinol metabolism,11/91,0.4755048051116466,0.6657067271563052,0,0,1.06104969352014,0.7887613085358794,CYP26B1;CYP2A5;RETSAT;ALDH1A1;RDH5;UGT2A2;CYP2B19;UGT1A6A;UGT1A7C;RPE65;DHRS3
+KEGG_2019_Mouse,Glyoxylate and dicarboxylate metabolism,4/31,0.4829840495015984,0.669798634686179,0,0,1.143152755549089,0.831954166743429,ACSS2;HOGA1;ACO2;ACAT2
+KEGG_2019_Mouse,Propanoate metabolism,4/31,0.4829840495015984,0.669798634686179,0,0,1.143152755549089,0.831954166743429,ACSS3;ACSS2;SUCLG2;ACAT2
+KEGG_2019_Mouse,Small cell lung cancer,11/92,0.4905930063529715,0.6771565439801578,0,0,1.047890856413915,0.7462454163181425,CDK6;MYC;AKT2;COL4A4;COL4A3;CDK2;AKT1;PIK3R3;NFKB1;PTK2;BIRC3
+KEGG_2019_Mouse,mTOR signaling pathway,18/154,0.504461385650312,0.6872652704822437,0,0,1.021215996279935,0.6987813240543326,MAP2K1;WNT10A;PRKAA2;CAB39;PIK3R3;CASTOR1;IGF1;TNF;TNFRSF1A;RPS6KA3;DEPTOR;AKT2;RPS6KA1;EIF4EBP1;AKT1;MAPK1;KRAS;FNIP2
+KEGG_2019_Mouse,Pentose phosphate pathway,4/32,0.5086850001526625,0.6872652704822437,0,0,1.1022635156201284,0.7450489154073501,PGD;FBP1;PFKP;DERA
+KEGG_2019_Mouse,beta-Alanine metabolism,4/32,0.5086850001526625,0.6872652704822437,0,0,1.1022635156201284,0.7450489154073501,ALDH3B1;GAD1;SRM;CNDP2
+KEGG_2019_Mouse,Aldosterone synthesis and secretion,12/102,0.5094378322069351,0.6872652704822437,0,0,1.0287633231128632,0.6938468009786937,NR4A2;GNA11;ATP1B3;ADCY2;CACNA1S;ATP1B1;PLCB2;ADCY7;CAMK2G;CAMK1G;AGT;KCNK3
+KEGG_2019_Mouse,Signaling pathways regulating pluripotency of stem cells,16/137,0.5099847580368089,0.6872652704822437,0,0,1.020253192098898,0.6870124217987424,ACVR1;MAP2K1;WNT10A;STAT3;PIK3R3;IGF1;FGF2;MAPK11;ACVR1C;MYC;AKT2;AKT1;ID3;MAPK1;KRAS;JAK3
+KEGG_2019_Mouse,Porphyrin and chlorophyll metabolism,5/41,0.514338389350747,0.6872652704822437,0,0,1.0716278505579817,0.7124973723825312,HMOX1;UGT2A2;GUSB;UGT1A6A;UGT1A7C
+KEGG_2019_Mouse,PPAR signaling pathway,10/85,0.5169871316412271,0.6872652704822437,0,0,1.0287381473377095,0.6786969228203672,CYP27A1;CPT2;ACOX2;OLR1;LPL;APOC3;DBI;PLIN2;CYP8B1;PLIN5
+KEGG_2019_Mouse,Phosphonate and phosphinate metabolism,1/6,0.5187686154005884,0.6872652704822437,0,0,1.5431560592850917,1.0127691906360798,PCYT2
+KEGG_2019_Mouse,Cysteine and methionine metabolism,6/50,0.5189554083233269,0.6872652704822437,0,0,1.0521267723102583,0.6901292131826965,IL4I1;BHMT;CBS;BCAT1;SDSL;SRM
+KEGG_2019_Mouse,Amphetamine addiction,8/68,0.5264152879671565,0.6885397246271638,0,0,1.0287130155953943,0.660088989945198,PPP3CA;PPP3R1;PPP3CC;TH;FOSB;FOS;CAMK2G;GRIA4
+KEGG_2019_Mouse,Renal cell carcinoma,8/68,0.5264152879671565,0.6885397246271638,0,0,1.0287130155953943,0.660088989945198,MAP2K1;PAK1;AKT2;AKT1;PIK3R3;MAPK1;KRAS;HIF1A
+KEGG_2019_Mouse,Thiamine metabolism,2/15,0.526943666806503,0.6885397246271638,0,0,1.1870240531383138,0.7604807650410265,AK1;AK7
+KEGG_2019_Mouse,Starch and sucrose metabolism,4/33,0.5337908626981325,0.6944005028019954,0,0,1.0641942232724757,0.6680491577217341,HK3;G6PC3;HK2;GANC
+KEGG_2019_Mouse,Alzheimer disease,20/175,0.5432355678578823,0.7010181621266453,0,0,0.9953917050691244,0.6074001885877025,MME;LPL;ATP2A1;TNF;COX6A2;COX7A1;RTN4;TNFRSF1A;PPP3CA;ADAM17;PPP3R1;CASP8;PPP3CC;CASP12;MAPK1;FAS;CACNA1S;APOE;APBB1;PLCB2
+KEGG_2019_Mouse,Cortisol synthesis and secretion,8/69,0.5436467379757657,0.7010181621266453,0,0,1.011791523006014,0.6166420317703202,GNA11;ADCY2;CACNA1S;PLCB2;ADCY7;AGT;PBX1;KCNK3
+KEGG_2019_Mouse,Glycerolipid metabolism,7/61,0.5596126450243568,0.7184546621710084,0,0,1.0000404687904687,0.5805339324604427,LIPC;AKR1B10;LPL;PLPP2;AGPAT2;AKR1B8;PLPP1
+KEGG_2019_Mouse,Base excision repair,4/35,0.5819755478722367,0.743916569888859,0,0,0.9954238887089734,0.5388496742059723,PARP3;TDG;XRCC1;UNG
+KEGG_2019_Mouse,Arrhythmogenic right ventricular cardiomyopathy (ARVC),8/72,0.5937201433942607,0.7513810959017064,0,0,0.9641998250983822,0.5026828884152847,TCF7L2;CACNB3;CACNB4;ITGB5;CACNA2D1;CTNNA3;ITGA5;CACNA1S
+KEGG_2019_Mouse,Mitophagy,7/63,0.5949907428826617,0.7513810959017064,0,0,0.964215472027972,0.5006297673045704,MAPK9;MAPK8;CITED2;TAX1BP1;KRAS;RAB7B;HIF1A
+KEGG_2019_Mouse,Hepatocellular carcinoma,19/171,0.5954822970921687,0.7513810959017064,0,0,0.9640268014059754,0.4997357015797842,TCF7L2;MAP2K1;WNT10A;SHC1;CSNK1A1;PIK3R3;TGFBR1;TGFBR2;FRAT1;CDK6;MYC;AKT2;DPF1;PLCG2;AKT1;HMOX1;MAPK1;KRAS;NFE2L2
+KEGG_2019_Mouse,Renin-angiotensin system,4/36,0.6049586224157699,0.7583360737499022,0,0,0.9642623308598866,0.4846336344508856,CTSA;MME;PRCP;AGT
+KEGG_2019_Mouse,Melanogenesis,11/100,0.6061529841198198,0.7583360737499022,0,0,0.953265510930951,0.4772265214165886,TCF7L2;MAP2K1;WNT10A;MAPK1;ADCY2;KRAS;PLCB2;ADCY7;CAMK2G;GNAI1;GNAI2
+KEGG_2019_Mouse,Ascorbate and aldarate metabolism,3/27,0.6131517290119819,0.7606186005465092,0,0,0.9642779232111692,0.471669656270675,UGT2A2;UGT1A6A;UGT1A7C
+KEGG_2019_Mouse,Collecting duct acid secretion,3/27,0.6131517290119819,0.7606186005465092,0,0,0.9642779232111692,0.471669656270675,TCIRG1;ATP6V0D2;ATP6V0E
+KEGG_2019_Mouse,"Phenylalanine, tyrosine and tryptophan biosynthesis",1/8,0.6229061131080822,0.7694722573688074,0,0,1.1021297795491345,0.5217035714292436,IL4I1
+KEGG_2019_Mouse,"Alanine, aspartate and glutamate metabolism",4/37,0.6271538383336417,0.7714779433895006,0,0,0.9349893522743806,0.4362318226146635,IL4I1;GLS2;GAD1;GLS
+KEGG_2019_Mouse,Tryptophan metabolism,5/48,0.6583636666585085,0.8031892479430368,0,0,0.8968213669137809,0.3748693722094382,IL4I1;HAAO;KYNU;DLST;ACAT2
+KEGG_2019_Mouse,Arginine biosynthesis,2/19,0.6583966284158906,0.8031892479430368,0,0,0.9075190477412072,0.379295544546058,GLS2;GLS
+KEGG_2019_Mouse,Sulfur metabolism,1/9,0.6661948133945143,0.8093441121404429,0,0,0.9643090671316478,0.3916764403877555,SQOR
+KEGG_2019_Mouse,Notch signaling pathway,5/49,0.6765272508995048,0.8185144517055738,0,0,0.8763894402540691,0.3424777002517023,ADAM17;HDAC2;MAML2;DTX3L;MFNG
+KEGG_2019_Mouse,"Glycine, serine and threonine metabolism",4/40,0.6887931366435863,0.8299392712016982,0,0,0.856928076046365,0.3194750323728569,BHMT;THA1;CBS;SDSL
+KEGG_2019_Mouse,Purine metabolism,14/136,0.7070085934113748,0.8484103120936497,0,0,0.8845775148949626,0.3066940448390294,ENTPD1;GDA;PDE1A;AK1;PDE3B;PDE4C;GMPS;PRUNE1;ADCY2;AK7;ADCY7;PNP;IMPDH1;PDE5A
+KEGG_2019_Mouse,Ubiquinone and other terpenoid-quinone biosynthesis,1/11,0.7384396511674602,0.882525436761111,0,0,0.7713600697471665,0.2338886365930831,VKORC1
+KEGG_2019_Mouse,Glutathione metabolism,6/64,0.7578858885383051,0.8963799237633175,0,0,0.7975324264473268,0.2210938912186899,HPGDS;GGT7;LAP3;PGD;NAT8F7;SRM
+KEGG_2019_Mouse,Phenylalanine metabolism,2/23,0.7588750618984832,0.8963799237633175,0,0,0.7344921396382365,0.202659693236337,IL4I1;ALDH3B1
+KEGG_2019_Mouse,Hedgehog signaling pathway,4/44,0.7591789150240342,0.8963799237633175,0,0,0.7710606721955477,0.2124409436906275,CSNK1G3;CSNK1A1;SPOPL;LRP2
+KEGG_2019_Mouse,Cell cycle,12/123,0.7659054816560192,0.9007048464274786,0,0,0.8331380000236768,0.2221949960790216,CDC20;CCNB2;HDAC2;CDK6;TFDP1;RBL1;CHEK2;MYC;CDK2;CDK1;MCM3;YWHAZ
+KEGG_2019_Mouse,Protein processing in endoplasmic reticulum,16/163,0.782023507617313,0.9159956623087252,0,0,0.8385585036998564,0.2061767801330832,PPP1R15A;HSP90AA1;SEC23A;EDEM2;DERL1;EIF2AK2;CKAP4;SEC61A2;MAPK9;MAPK8;CASP12;DNAJA2;SEC62;CRYAB;SEC63;NFE2L2
+KEGG_2019_Mouse,Proteasome,4/46,0.789424545859679,0.9209390603694216,0,0,0.7342603562594833,0.1736166115873263,PSME1;PSMB8;PSMA8;PSMB9
+KEGG_2019_Mouse,Synaptic vesicle cycle,7/77,0.7948688471611232,0.9209390603694216,0,0,0.7707604895104895,0.1769497673223731,SLC17A6;TCIRG1;STX3;ATP6V0D2;CPLX1;VAMP2;ATP6V0E
+KEGG_2019_Mouse,Nicotinate and nicotinamide metabolism,3/36,0.7982856491119206,0.9209390603694216,0,0,0.7009360621926067,0.1579130368300381,PNP;NAPRT;CD38
+KEGG_2019_Mouse,alpha-Linolenic acid metabolism,2/25,0.7987736748102128,0.9209390603694216,0,0,0.6705474127306168,0.1506570061132003,PLA2G4A;PLA2G5
+KEGG_2019_Mouse,Taste transduction,8/88,0.8054555619253108,0.9250153718985992,0,0,0.7706602536073459,0.1667302236223226,GRM4;GABRA6;GABRA5;PDE1A;GABRA3;HTR3A;ASIC2;TRPM5
+KEGG_2019_Mouse,Steroid hormone biosynthesis,8/89,0.815302035378368,0.932680149421168,0,0,0.7611027439040848,0.1554146221160243,SULT2B1;UGT2A2;CYP2E1;CYP7B1;HSD17B7;CYP2B19;UGT1A6A;UGT1A7C
+KEGG_2019_Mouse,Lysine degradation,5/59,0.8221274278323817,0.9368428828787604,0,0,0.7136907650008086,0.1397833834840775,SETD3;PHYKPL;DLST;COLGALT1;ACAT2
+KEGG_2019_Mouse,Maturity onset diabetes of the young,2/27,0.832702078925738,0.941593889246796,0,0,0.6168338421282163,0.1129295383413813,NEUROD1;HHEX
+KEGG_2019_Mouse,Phototransduction,2/27,0.832702078925738,0.941593889246796,0,0,0.6168338421282163,0.1129295383413813,GNB1;GUCA1A
+KEGG_2019_Mouse,Tyrosine metabolism,3/40,0.8530361196703783,0.9601052621698288,0,0,0.625017687844913,0.0993486791100603,IL4I1;TH;ALDH3B1
+KEGG_2019_Mouse,Drug metabolism,10/114,0.855995279868405,0.9601052621698288,0,0,0.7406581383605454,0.1151652428088797,HPGDS;UCK2;IMPDH1;ALDH3B1;GMPS;UGT2A2;CYP2E1;GUSB;UGT1A6A;UGT1A7C
+KEGG_2019_Mouse,Chemical carcinogenesis,8/94,0.8588696733015816,0.9601052621698288,0,0,0.7166492104005451,0.1090296403493235,HPGDS;ALDH3B1;EPHX1;UGT2A2;CYP2E1;CYP2B19;UGT1A6A;UGT1A7C
+KEGG_2019_Mouse,Circadian rhythm,2/30,0.8739564697673508,0.973269704968186,0,0,0.55065104977883,0.0741863031716813,PRKAA2;CRY1
+KEGG_2019_Mouse,Ubiquitin mediated proteolysis,12/138,0.8805180410415133,0.9768766191177544,0,0,0.7333291617128673,0.0933123677730202,CDC20;CUL4A;SOCS3;HERC3;SOCS1;UBE3C;UBA7;FBXW7;UBE2C;UBE2QL1;UBE2L6;BIRC3
+KEGG_2019_Mouse,Alcoholism,18/199,0.8863441046226878,0.979643484056655,0,0,0.7653571506562138,0.0923403584740937,MAP2K1;HDAC2;SHC1;BDNF;HDAC9;GNAI1;GNAI2;CAMKK2;GNGT2;TH;GNG5;NPY;GNB1;CRH;FOSB;MAPK1;KRAS;SLC29A1
+KEGG_2019_Mouse,Endocrine and other factor-regulated calcium reabsorption,4/55,0.8896812002541685,0.9796489620776238,0,0,0.6043768882498438,0.0706468730142244,BDKRB2;ATP1B3;ATP1B1;PLCB2
+KEGG_2019_Mouse,"Valine, leucine and isoleucine degradation",4/56,0.8977182453129324,0.9848103138880676,0,0,0.592720679582312,0.0639539792252542,IL4I1;BCAT1;ACADS;ACAT2
+KEGG_2019_Mouse,Pyrimidine metabolism,4/58,0.9122573473627712,0.9970396287161885,0,0,0.570703396543641,0.0524094903846021,ENTPD1;UCK2;PNP;TYMS
+KEGG_2019_Mouse,DNA replication,2/35,0.9224488162113504,0.9999959073849164,0,0,0.4670869180245543,0.0377048386948698,MCM3;DNA2
+KEGG_2019_Mouse,Mismatch repair,1/22,0.9316388545202844,0.9999959073849164,0,0,0.3670859799892058,0.0259933709229567,MSH2
+KEGG_2019_Mouse,mRNA surveillance pathway,7/96,0.9343835095147314,0.9999959073849164,0,0,0.6055629763494932,0.0410985389956953,PPP2R2C;PPP2R1A;WDR82;PPP2R2B;CSTF2;CSTF2T;RNPS1
+KEGG_2019_Mouse,Fatty acid degradation,3/50,0.9369190225387029,0.9999959073849164,0,0,0.4917567115963016,0.0320420915955614,CPT2;ACADS;ACAT2
+KEGG_2019_Mouse,Linoleic acid metabolism,3/50,0.9369190225387029,0.9999959073849164,0,0,0.4917567115963016,0.0320420915955614,PLA2G4A;CYP2E1;PLA2G5
+KEGG_2019_Mouse,Vitamin digestion and absorption,1/24,0.9464428056975812,0.9999959073849164,0,0,0.3351275539213828,0.0184470081722111,TCN2
+KEGG_2019_Mouse,Glycosylphosphatidylinositol (GPI)-anchor biosynthesis,1/25,0.9525956458567836,0.9999959073849164,0,0,0.3211457425167102,0.0155963663679517,PIGW
+KEGG_2019_Mouse,RNA polymerase,1/28,0.9671296231237466,0.9999959073849164,0,0,0.2854144467047693,0.0095393345137457,POLR3F
+KEGG_2019_Mouse,RNA degradation,5/83,0.968413769073405,0.9999959073849164,0,0,0.4934217892733176,0.015836784652321,PNLDC1;TENT4A;ENO2;TENT4B;PFKP
+KEGG_2019_Mouse,MicroRNAs in cancer,23/281,0.9715047884260146,0.9999959073849164,0,0,0.6845742575608691,0.0197904128520611,ST14;MAP2K1;SHC1;GLS2;CDCA5;STAT3;TPM1;THBS1;NFKB1;GLS;SOCS1;CDK6;PLAU;MYC;PLCG2;PDCD4;HMOX1;SPRY2;KRAS;ITGA5;VIM;ABCB1B;CD44
+KEGG_2019_Mouse,N-Glycan biosynthesis,2/50,0.9832150388678954,0.9999959073849164,0,0,0.3208496874545719,0.0054311590236506,ST6GAL1;B4GALT1
+KEGG_2019_Mouse,Fanconi anemia pathway,2/51,0.9848874586737985,0.9999959073849164,0,0,0.3142839342453074,0.0047858841623768,FANCI;RMI1
+KEGG_2019_Mouse,Peroxisome,4/84,0.9901632869178488,0.9999959073849164,0,0,0.3846573548668703,0.0038024968830226,MVK;ACOX2;HACL1;SOD2
+KEGG_2019_Mouse,Basal transcription factors,1/43,0.9947339768794412,0.9999959073849164,0,0,0.1833250300992236,0.00096794469941175,TAF2
+KEGG_2019_Mouse,Nucleotide excision repair,1/43,0.9947339768794412,0.9999959073849164,0,0,0.1833250300992236,0.00096794469941175,CUL4A
+KEGG_2019_Mouse,Parkinson disease,8/144,0.9950648573201972,0.9999959073849164,0,0,0.4518891946809331,0.0022356588477158,UBA7;TH;UBE2L6;VDAC1;COX6A2;COX7A1;GNAI1;GNAI2
+KEGG_2019_Mouse,Basal cell carcinoma,2/63,0.9957977321790968,0.9999959073849164,0,0,0.2522860023020883,0.001062407175601,TCF7L2;WNT10A
+KEGG_2019_Mouse,Spliceosome,6/132,0.9984202321582688,0.9999959073849164,0,0,0.3657034679315151,0.0005781833963570268,PRPF18;ZMAT2;DHX15;SRSF4;U2AF1;PQBP1
+KEGG_2019_Mouse,Huntington disease,10/192,0.9991448278826572,0.9999959073849164,0,0,0.4213576357996489,0.0003604874629574526,HDAC2;CASP8;BDNF;VDAC1;SOD2;COX6A2;COX7A1;PLCB2;DNAL1;DNALI1
+KEGG_2019_Mouse,Oxidative phosphorylation,5/134,0.9996332149186756,0.9999959073849164,0,0,0.2974848515622355,0.00010913302087870076,TCIRG1;COX6A2;ATP6V0D2;COX7A1;ATP6V0E
+KEGG_2019_Mouse,Thermogenesis,11/231,0.9998873331375302,0.9999959073849164,0,0,0.3827714535901926,4.312808831783397e-05,RPS6KA3;MAPK11;PRKAA2;CPT2;DPF1;RPS6KA1;ADCY2;KRAS;ADCY7;COX6A2;COX7A1
+KEGG_2019_Mouse,RNA transport,5/167,0.9999812928723458,0.9999959073849164,0,0,0.2364413175912448,4.423179283518713e-06,FMR1;EIF4EBP1;TACC3;NUP153;RNPS1
+KEGG_2019_Mouse,Ribosome biogenesis in eukaryotes,2/115,0.9999839024194264,0.9999959073849164,0,0,0.1357884133704348,2.1858825189390733e-06,GNL3L;IMP4
+KEGG_2019_Mouse,Olfactory transduction,3/1133,0.9999930122614734,0.9999959073849164,0,0,0.0191991382106287,1.3415902648632536e-07,PDE1A;GNB1;CAMK2G
+KEGG_2019_Mouse,Ribosome,2/170,0.9999959073849164,0.9999959073849164,0,0,0.0910483251303137,3.72626511274369e-07,RPL39L;MRPS5

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/enrich_ps3_kegg.csv

@@ -0,0 +1,254 @@
+Gene_set,Term,Overlap,P-value,Adjusted P-value,Old P-value,Old Adjusted P-value,Odds Ratio,Combined Score,Genes
+KEGG_2019_Mouse,Other types of O-glycan biosynthesis,4/22,0.0101887780324461,0.999994660071484,0,0,5.39746835443038,24.75531781683061,B4GALT2;POGLUT1;B3GLCT;OGT
+KEGG_2019_Mouse,Protein processing in endoplasmic reticulum,11/163,0.0605571508429523,0.999994660071484,0,0,1.7610573368286615,4.938300133141348,NSFL1C;HSPA5;HSPA4L;NGLY1;SSR3;DERL1;EIF2AK2;HYOU1;MARCH6;PDIA6;SVIP
+KEGG_2019_Mouse,Ubiquinone and other terpenoid-quinone biosynthesis,2/11,0.0682762332440027,0.999994660071484,0,0,5.386363636363637,14.458042524717747,COQ3;COQ2
+KEGG_2019_Mouse,Glutamatergic synapse,8/114,0.0839412377138514,0.999994660071484,0,0,1.8339814681453743,4.543942682886352,GNAO1;GRIN3A;DLG4;SLC1A2;ADCY3;SLC1A6;PLD1;GNG12
+KEGG_2019_Mouse,Selenocompound metabolism,2/17,0.1447181568869057,0.999994660071484,0,0,3.230808080808081,6.245045964707643,TXNRD1;PSTK
+KEGG_2019_Mouse,Morphine addiction,6/92,0.15883331435568,0.999994660071484,0,0,1.692834376106717,3.114645907720276,GABRA2;GNAO1;GRK5;ADCY3;PDE8B;GNG12
+KEGG_2019_Mouse,Base excision repair,3/35,0.1607832016867621,0.999994660071484,0,0,2.2725189633375478,4.153479261482016,PARP3;PARP4;LIG1
+KEGG_2019_Mouse,Hippo signaling pathway,9/159,0.1819166179042629,0.999994660071484,0,0,1.4565095541401274,2.4821935450816746,DLG1;TGFB1;FZD2;PARD6A;DLG4;YWHAB;BTRC;BMPR1B;LIMD1
+KEGG_2019_Mouse,Lysosome,7/124,0.2229203791880803,0.999994660071484,0,0,1.4511777929821132,2.178131689474954,GGA2;GM2A;FUCA1;PSAP;TCIRG1;CD68;AP1M1
+KEGG_2019_Mouse,Mannose type O-glycan biosynthesis,2/23,0.2315799652394364,0.999994660071484,0,0,2.306998556998557,3.374746798103913,B4GALT2;POMK
+KEGG_2019_Mouse,Hypertrophic cardiomyopathy (HCM),5/86,0.2559423525857411,0.999994660071484,0,0,1.49626813124912,2.0391187655112697,TGFB1;ACE;LAMA2;PRKAB1;CACNG5
+KEGG_2019_Mouse,alpha-Linolenic acid metabolism,2/25,0.2613631306402563,0.999994660071484,0,0,2.1061703996486605,2.826153237372389,FADS2;PLA2G6
+KEGG_2019_Mouse,Fc gamma R-mediated phagocytosis,5/87,0.2635116067051573,0.999994660071484,0,0,1.4779436767751708,1.9710712067165568,LIMK2;INPPL1;PLCG1;PLD1;PLA2G6
+KEGG_2019_Mouse,Taste transduction,5/88,0.2711312790029847,0.999994660071484,0,0,1.460060775421076,1.9056014616794097,GABRA2;HTR1A;HTR1B;SCN3A;TAS2R137
+KEGG_2019_Mouse,Riboflavin metabolism,1/8,0.2768463670886862,0.999994660071484,0,0,3.45865609800036,4.44192628771845,ACP1
+KEGG_2019_Mouse,Mucin type O-glycan biosynthesis,2/28,0.306023837082694,0.999994660071484,0,0,1.8628593628593628,2.205797392337885,GALNT16;GCNT4
+KEGG_2019_Mouse,Cholesterol metabolism,3/49,0.3081479395235083,0.999994660071484,0,0,1.5797284669927991,1.859617313407651,CYP27A1;TSPO;ANGPTL4
+KEGG_2019_Mouse,Fanconi anemia pathway,3/51,0.330102729560645,0.999994660071484,0,0,1.5137484197218711,1.677765136752001,FANCL;TELO2;FANCG
+KEGG_2019_Mouse,Circadian rhythm,2/30,0.3355306737678538,0.999994660071484,0,0,1.7296176046176046,1.8888148947387335,BTRC;PRKAB1
+KEGG_2019_Mouse,Glycerophospholipid metabolism,5/97,0.3413465697418953,0.999994660071484,0,0,1.316608805863228,1.4151661705228804,CHKB;PNPLA7;ETNK1;PLA2G6;PLD1
+KEGG_2019_Mouse,Glyoxylate and dicarboxylate metabolism,2/31,0.3501481587884255,0.999994660071484,0,0,1.6698885405781958,1.7523792030247451,GRHPR;DLD
+KEGG_2019_Mouse,Glycosaminoglycan biosynthesis,3/53,0.3520379800652464,0.999994660071484,0,0,1.453046776232617,1.5170043901523862,B4GALT2;NDST1;UST
+KEGG_2019_Mouse,Synaptic vesicle cycle,4/77,0.365750760311565,0.999994660071484,0,0,1.327067799549159,1.334768986732359,SLC1A2;TCIRG1;SLC1A6;AP2A2
+KEGG_2019_Mouse,Chagas disease (American trypanosomiasis),5/103,0.3889181271848207,0.999994660071484,0,0,1.235612115568661,1.1668953115698055,GNAO1;TGFB1;ACE;CCL3;IL12A
+KEGG_2019_Mouse,Prion diseases,2/34,0.3932743392123864,0.999994660071484,0,0,1.5130997474747474,1.4120970808594169,STIP1;HSPA5
+KEGG_2019_Mouse,Non-homologous end-joining,1/13,0.4094992507369164,0.999994660071484,0,0,2.0170239596469104,1.8008397459344894,LIG4
+KEGG_2019_Mouse,Renin-angiotensin system,2/36,0.421294100864465,0.999994660071484,0,0,1.4239453357100416,1.2308926828073612,ACE;ATP6AP2
+KEGG_2019_Mouse,Axon guidance,8/180,0.4235248933656133,0.999994660071484,0,0,1.1263388366175513,0.9676861119401772,PARD6A;RASA1;LIMK2;PLXNB1;PLCG1;LRRC4C;BMPR1B;SSH3
+KEGG_2019_Mouse,Toxoplasmosis,5/108,0.4284085297130833,0.999994660071484,0,0,1.1753233169675268,0.9962957539149996,GNAO1;TGFB1;LAMA2;IL12A;NFKBIB
+KEGG_2019_Mouse,PPAR signaling pathway,4/85,0.4378706250898923,0.999994660071484,0,0,1.1954992967651197,0.9872813226909506,CYP27A1;FADS2;CPT1A;ANGPTL4
+KEGG_2019_Mouse,Pyruvate metabolism,2/38,0.4486332443933831,0.999994660071484,0,0,1.3446969696969695,1.0778412545198457,GRHPR;DLD
+KEGG_2019_Mouse,Thiamine metabolism,1/15,0.4554846530609762,0.999994660071484,0,0,1.7286975319762206,1.359436079705838,ACP1
+KEGG_2019_Mouse,cAMP signaling pathway,9/211,0.4611821014332201,0.999994660071484,0,0,1.0786151226587626,0.8348074411779992,GRIN3A;ADORA2A;HTR1A;ADCY3;HTR1B;SSTR1;PLD1;RAPGEF4;DRD5
+KEGG_2019_Mouse,Ubiquitin mediated proteolysis,6/138,0.469333850960332,0.999994660071484,0,0,1.100253807106599,0.8322770109211333,UBE2W;FANCL;UBE4A;TRIM37;BTRC;TRIM32
+KEGG_2019_Mouse,"Glycine, serine and threonine metabolism",2/40,0.4752279474852387,0.999994660071484,0,0,1.2737905369484317,0.947650100304914,GRHPR;DLD
+KEGG_2019_Mouse,Nicotine addiction,2/40,0.4752279474852387,0.999994660071484,0,0,1.2737905369484317,0.947650100304914,GABRA2;GRIN3A
+KEGG_2019_Mouse,Primary bile acid biosynthesis,1/16,0.4771180925215306,0.999994660071484,0,0,1.6133669609079446,1.193877426551763,CYP27A1
+KEGG_2019_Mouse,Antigen processing and presentation,4/90,0.4817694108602536,0.999994660071484,0,0,1.1256991463055637,0.822086469451686,HSPA5;HSPA4;H2-Q4;TAPBP
+KEGG_2019_Mouse,Dilated cardiomyopathy (DCM),4/90,0.4817694108602536,0.999994660071484,0,0,1.1256991463055637,0.822086469451686,TGFB1;LAMA2;ADCY3;CACNG5
+KEGG_2019_Mouse,GABAergic synapse,4/90,0.4817694108602536,0.999994660071484,0,0,1.1256991463055637,0.822086469451686,GABRA2;GNAO1;ADCY3;GNG12
+KEGG_2019_Mouse,Oocyte meiosis,5/116,0.4903497765054139,0.999994660071484,0,0,1.090158599664303,0.7768866049734288,MOS;YWHAB;ADCY3;PPP2R5D;BTRC
+KEGG_2019_Mouse,Nitrogen metabolism,1/17,0.4978930787706773,0.999994660071484,0,0,1.5124527112232031,1.054739035716822,CAR14
+KEGG_2019_Mouse,Leishmaniasis,3/67,0.5000717646329553,0.999994660071484,0,0,1.1343631479140328,0.7861178150810111,TGFB1;IL12A;NFKBIB
+KEGG_2019_Mouse,Basal transcription factors,2/43,0.5136186434315607,0.999994660071484,0,0,1.1804015767430402,0.7864711489780748,TAF13;4933416C03RIK
+KEGG_2019_Mouse,Intestinal immune network for IgA production,2/43,0.5136186434315607,0.999994660071484,0,0,1.1804015767430402,0.7864711489780748,CCL25;TGFB1
+KEGG_2019_Mouse,Vasopressin-regulated water reabsorption,2/43,0.5136186434315607,0.999994660071484,0,0,1.1804015767430402,0.7864711489780748,ADCY3;DYNLL1
+KEGG_2019_Mouse,Other glycan degradation,1/18,0.5178436354613557,0.999994660071484,0,0,1.4234107262072546,0.936720898291026,FUCA1
+KEGG_2019_Mouse,Cortisol synthesis and secretion,3/69,0.5198837553116666,0.999994660071484,0,0,1.099873577749684,0.7194823447420737,ADCY3;PDE8B;CACNA1H
+KEGG_2019_Mouse,Chemokine signaling pathway,8/197,0.5247063660060708,0.999994660071484,0,0,1.0241124439597722,0.6604669880834272,CCL25;GRK5;PXN;CCL3;ADCY3;GNG12;PF4;NFKBIB
+KEGG_2019_Mouse,Mineral absorption,2/44,0.5259970939221404,0.999994660071484,0,0,1.1522366522366525,0.7402654884723641,SLC40A1;SLC26A6
+KEGG_2019_Mouse,mRNA surveillance pathway,4/96,0.5325469270201381,0.999994660071484,0,0,1.0519537699504675,0.6628195120854194,HBS1L;FIP1L1;PPP2R5D;PPP2R3A
+KEGG_2019_Mouse,Alcoholism,8/199,0.5362383217907123,0.999994660071484,0,0,1.0132821763052369,0.6314537278150026,GNAO1;GRIN3A;ADORA2A;HIST1H2BJ;HIST2H3C2;GNG12;SLC29A1;HIST1H2BA
+KEGG_2019_Mouse,Glycosphingolipid biosynthesis,2/45,0.5381607227996654,0.999994660071484,0,0,1.1253817242189337,0.697284290513801,B4GALT2;B3GALT5
+KEGG_2019_Mouse,Adipocytokine signaling pathway,3/71,0.5392616314174665,0.999994660071484,0,0,1.0674128058302967,0.6591855008224219,CPT1A;PRKAB1;NFKBIB
+KEGG_2019_Mouse,Adherens junction,3/72,0.5487805982401147,0.999994660071484,0,0,1.0518880888253723,0.6311923441476909,SNAI1;BAIAP2;ACP1
+KEGG_2019_Mouse,Proteasome,2/46,0.5501070596308211,0.999994660071484,0,0,1.0997474747474747,0.6572556826170588,PSMB11;PSMD13
+KEGG_2019_Mouse,Circadian entrainment,4/99,0.5569928197426123,0.999994660071484,0,0,1.0185742838107927,0.5960726553950803,GNAO1;ADCY3;GNG12;CACNA1H
+KEGG_2019_Mouse,Toll-like receptor signaling pathway,4/99,0.5569928197426123,0.999994660071484,0,0,1.0185742838107927,0.5960726553950803,SPP1;CCL3;IL12A;LBP
+KEGG_2019_Mouse,Thyroid hormone synthesis,3/73,0.5581829047285021,0.999994660071484,0,0,1.036806935163446,0.6045295516285165,HSPA5;GSR;ADCY3
+KEGG_2019_Mouse,Ether lipid metabolism,2/47,0.5618341678521759,0.999994660071484,0,0,1.0752525252525251,0.6199352817674322,PLA2G6;PLD1
+KEGG_2019_Mouse,Cocaine addiction,2/48,0.5733406062096127,0.999994660071484,0,0,1.051822573561704,0.5851029309854827,GRIN3A;DLG4
+KEGG_2019_Mouse,Th17 cell differentiation,4/102,0.5807356024263919,0.999994660071484,0,0,0.9872384396796692,0.5365243052536757,TGFB1;RARA;PLCG1;NFKBIB
+KEGG_2019_Mouse,Amino sugar and nucleotide sugar metabolism,2/49,0.584625392855167,0.999994660071484,0,0,1.029389641091769,0.5525598797963339,CYB5R3;GFPT1
+KEGG_2019_Mouse,Malaria,2/49,0.584625392855167,0.999994660071484,0,0,1.029389641091769,0.5525598797963339,TGFB1;IL12A
+KEGG_2019_Mouse,Notch signaling pathway,2/49,0.584625392855167,0.999994660071484,0,0,1.029389641091769,0.5525598797963339,ADAM17;NCSTN
+KEGG_2019_Mouse,Mismatch repair,1/22,0.5900360033380718,0.999994660071484,0,0,1.152044676634841,0.6077861931096862,LIG1
+KEGG_2019_Mouse,Autophagy,5/130,0.5919154072524235,0.999994660071484,0,0,0.9673510773130544,0.507270728372213,UVRAG;ATG16L2;IGBP1B;RRAGD;ZFYVE1
+KEGG_2019_Mouse,N-Glycan biosynthesis,2/50,0.5956879709979176,0.999994660071484,0,0,1.007891414141414,0.5221263422736672,B4GALT2;DPM1
+KEGG_2019_Mouse,Relaxin signaling pathway,5/131,0.5987375750491811,0.999994660071484,0,0,0.9596233930834692,0.4922214329832576,GNAO1;TGFB1;ADCY3;RLN1;GNG12
+KEGG_2019_Mouse,Serotonergic synapse,5/132,0.6054951726182619,0.999994660071484,0,0,0.9520174046685228,0.4776354035970594,GNAO1;CYP2D22;HTR1A;HTR1B;GNG12
+KEGG_2019_Mouse,Human immunodeficiency virus 1 infection,9/238,0.6065418510564768,0.999994660071484,0,0,0.9500903957945096,0.4750276675547442,GNAO1;LIMK2;PXN;H2-Q4;PLCG1;BTRC;GNG12;AP1M1;TAPBP
+KEGG_2019_Mouse,Amyotrophic lateral sclerosis (ALS),2/52,0.6171462096590109,0.999994660071484,0,0,0.9674747474747476,0.4669510236610784,SLC1A2;DERL1
+KEGG_2019_Mouse,Vitamin digestion and absorption,1/24,0.6219761119567314,0.999994660071484,0,0,1.051757223531992,0.4994306957109061,SLC52A3
+KEGG_2019_Mouse,Dopaminergic synapse,5/135,0.6253708733432658,0.999994660071484,0,0,0.9299015306619868,0.4365054568028811,GNAO1;PPP2R5D;PPP2R3A;GNG12;DRD5
+KEGG_2019_Mouse,Glycosylphosphatidylinositol (GPI)-anchor biosynthesis,1/25,0.6370016401563485,0.999994660071484,0,0,1.0078814627994956,0.4545374547202951,PIGH
+KEGG_2019_Mouse,Insulin resistance,4/110,0.6403081889219072,0.999994660071484,0,0,0.9123477430140912,0.4067297998979262,CPT1A;GFPT1;PRKAB1;OGT
+KEGG_2019_Mouse,Apelin signaling pathway,5/138,0.644633304501988,0.999994660071484,0,0,0.9087833652572496,0.3990228237966089,SPP1;ADCY3;PLAT;GNG12;PRKAB1
+KEGG_2019_Mouse,Folate biosynthesis,1/26,0.6514306598053069,0.999994660071484,0,0,0.967515762925599,0.4146620850454096,PCBD2
+KEGG_2019_Mouse,Rheumatoid arthritis,3/84,0.6534192208178323,0.999994660071484,0,0,0.8954909397387274,0.3810639581603806,TGFB1;CCL3;TCIRG1
+KEGG_2019_Mouse,Tight junction,6/167,0.6551734498152928,0.999994660071484,0,0,0.900699940095217,0.380865715929446,DLG1;PARD6A;HSPA4;HCLS1;PRKAB1;ACTR3B
+KEGG_2019_Mouse,Regulation of lipolysis in adipocytes,2/56,0.657411737820988,0.999994660071484,0,0,0.8956228956228957,0.3756643331888896,ADCY3;ABHD5
+KEGG_2019_Mouse,Cholinergic synapse,4/113,0.6611653572286089,0.999994660071484,0,0,0.887097898037394,0.3670379158091222,GNAO1;KCNJ4;ADCY3;GNG12
+KEGG_2019_Mouse,Butanoate metabolism,1/27,0.6652868233851852,0.999994660071484,0,0,0.930255116888156,0.3791133962730234,L2HGDH
+KEGG_2019_Mouse,Collecting duct acid secretion,1/27,0.6652868233851852,0.999994660071484,0,0,0.930255116888156,0.3791133962730234,TCIRG1
+KEGG_2019_Mouse,Legionellosis,2/58,0.6762387905131497,0.999994660071484,0,0,0.863546176046176,0.3378270574956183,HBS1L;IL12A
+KEGG_2019_Mouse,VEGF signaling pathway,2/58,0.6762387905131497,0.999994660071484,0,0,0.863546176046176,0.3378270574956183,PXN;PLCG1
+KEGG_2019_Mouse,Th1 and Th2 cell differentiation,3/87,0.6766847540983636,0.999994660071484,0,0,0.8633736680512913,0.3371903838354896,IL12A;PLCG1;NFKBIB
+KEGG_2019_Mouse,Protein export,1/28,0.6785928466633031,0.999994660071484,0,0,0.8957545187053384,0.3473144538942967,HSPA5
+KEGG_2019_Mouse,RNA polymerase,1/28,0.6785928466633031,0.999994660071484,0,0,0.8957545187053384,0.3473144538942967,POLR2B
+KEGG_2019_Mouse,Inflammatory bowel disease (IBD),2/59,0.6853320825488993,0.999994660071484,0,0,0.8483519404572036,0.3205512790197384,TGFB1;IL12A
+KEGG_2019_Mouse,Lysine degradation,2/59,0.6853320825488993,0.999994660071484,0,0,0.8483519404572036,0.3205512790197384,TMLHE;DLD
+KEGG_2019_Mouse,Viral carcinogenesis,8/229,0.6929303723667709,0.999994660071484,0,0,0.8743509147640266,0.3207344367298428,RBL2;DLG1;HIST1H2BJ;YWHAB;PXN;H2-Q4;EIF2AK2;HIST1H2BA
+KEGG_2019_Mouse,Proteoglycans in cancer,7/203,0.7000727022670568,0.999994660071484,0,0,0.8626792521328734,0.3076064803478825,FZD2;TGFB1;LUM;PXN;HCLS1;ANK3;PLCG1
+KEGG_2019_Mouse,Cytosolic DNA-sensing pathway,2/61,0.7028892152636298,0.999994660071484,0,0,0.8195086457798322,0.2889226803854119,TREX1;NFKBIB
+KEGG_2019_Mouse,Long-term depression,2/61,0.7028892152636298,0.999994660071484,0,0,0.8195086457798322,0.2889226803854119,GNAO1;CRHR1
+KEGG_2019_Mouse,Adrenergic signaling in cardiomyocytes,5/148,0.7042081108149008,0.999994660071484,0,0,0.8447889246368334,0.2962517244053106,ADCY3;PPP2R5D;PPP2R3A;RAPGEF4;CACNG5
+KEGG_2019_Mouse,Retrograde endocannabinoid signaling,5/150,0.7152468966596092,0.999994660071484,0,0,0.8330492548402605,0.2791777023892166,GNAO1;GABRA2;NDUFA4;ADCY3;GNG12
+KEGG_2019_Mouse,Propanoate metabolism,1/31,0.7154239493547337,0.999994660071484,0,0,0.8060529634300126,0.2699309969231139,DLD
+KEGG_2019_Mouse,Allograft rejection,2/63,0.7196235651859002,0.999994660071484,0,0,0.7925567146878623,0.2607725818007894,H2-Q4;IL12A
+KEGG_2019_Mouse,Cell cycle,4/123,0.7246257326245588,0.999994660071484,0,0,0.8121263695351558,0.2615858941117246,ORC5;RBL2;TGFB1;YWHAB
+KEGG_2019_Mouse,Biosynthesis of unsaturated fatty acids,1/32,0.7267391011634169,0.999994660071484,0,0,0.7800105764145955,0.2489698102029523,FADS2
+KEGG_2019_Mouse,Citrate cycle (TCA cycle),1/32,0.7267391011634169,0.999994660071484,0,0,0.7800105764145955,0.2489698102029523,DLD
+KEGG_2019_Mouse,Galactose metabolism,1/32,0.7267391011634169,0.999994660071484,0,0,0.7800105764145955,0.2489698102029523,B4GALT2
+KEGG_2019_Mouse,Pentose phosphate pathway,1/32,0.7267391011634169,0.999994660071484,0,0,0.7800105764145955,0.2489698102029523,RBKS
+KEGG_2019_Mouse,Rap1 signaling pathway,7/209,0.72818678889946,0.999994660071484,0,0,0.8367909217859524,0.2654281436692104,GNAO1;PARD6A;ADORA2A;ADCY3;PLCG1;SIPA1L3;RAPGEF4
+KEGG_2019_Mouse,Neuroactive ligand-receptor interaction,12/348,0.7312686567410913,0.999994660071484,0,0,0.8618012422360248,0.2697216985008405,GABRA2;GRIN3A;ADORA2A;HTR1A;TSPO;HTR1B;OPRK1;RLN1;SSTR1;CRHR1;ADRA2A;DRD5
+KEGG_2019_Mouse,Oxytocin signaling pathway,5/154,0.7364406152928616,0.999994660071484,0,0,0.8105153920092548,0.2479582807756496,GNAO1;KCNJ4;ADCY3;PRKAB1;CACNG5
+KEGG_2019_Mouse,SNARE interactions in vesicular transport,1/33,0.7376048873356247,0.999994660071484,0,0,0.7555958385876419,0.2299633121522807,VAMP4
+KEGG_2019_Mouse,AMPK signaling pathway,4/126,0.7418407249595614,0.999994660071484,0,0,0.7920315418136543,0.2365170255133641,CPT1A;PPP2R5D;PPP2R3A;PRKAB1
+KEGG_2019_Mouse,Cellular senescence,6/185,0.7478192115948229,0.999994660071484,0,0,0.8093610866914329,0.2351954966286278,RBL2;TGFB1;TRAF3IP2;H2-Q4;BTRC;HIPK1
+KEGG_2019_Mouse,Pentose and glucuronate interconversions,1/34,0.7480391324212297,0.999994660071484,0,0,0.7326607818411097,0.2126914149346372,DCXR
+KEGG_2019_Mouse,RIG-I-like receptor signaling pathway,2/68,0.7580051179117162,0.999994660071484,0,0,0.7323232323232324,0.2029012399874738,IL12A;NFKBIB
+KEGG_2019_Mouse,DNA replication,1/35,0.758058954332533,0.999994660071484,0,0,0.7110748460796676,0.1969635513861047,LIG1
+KEGG_2019_Mouse,Choline metabolism in cancer,3/99,0.7581347954192857,0.999994660071484,0,0,0.7549778761061947,0.2090489035046571,CHKB;PLCG1;PLD1
+KEGG_2019_Mouse,Cushing syndrome,5/159,0.7612834437258328,0.999994660071484,0,0,0.783994206047438,0.2138340498723343,FZD2;ADCY3;PDE8B;CRHR1;CACNA1H
+KEGG_2019_Mouse,Regulation of actin cytoskeleton,7/217,0.7627703469064105,0.999994660071484,0,0,0.804574332909784,0.2178773455000983,MOS;DIAPH1;LIMK2;PXN;GNG12;BAIAP2;SSH3
+KEGG_2019_Mouse,Melanogenesis,3/100,0.7641084136126405,0.999994660071484,0,0,0.7471554993678887,0.2010188975649325,GNAO1;FZD2;ADCY3
+KEGG_2019_Mouse,Type I diabetes mellitus,2/69,0.7651132084399163,0.999994660071484,0,0,0.7213553444896729,0.1931295276493877,H2-Q4;IL12A
+KEGG_2019_Mouse,Nicotinate and nicotinamide metabolism,1/36,0.7676807931158754,0.999994660071484,0,0,0.6907223923617366,0.1826140606838427,NMNAT1
+KEGG_2019_Mouse,AGE-RAGE signaling pathway in diabetic complications,3/101,0.7699616525431697,0.999994660071484,0,0,0.7394927629711809,0.1933141806288568,DIAPH1;TGFB1;PLCG1
+KEGG_2019_Mouse,T cell receptor signaling pathway,3/101,0.7699616525431697,0.999994660071484,0,0,0.7394927629711809,0.1933141806288568,DLG1;PLCG1;NFKBIB
+KEGG_2019_Mouse,Human papillomavirus infection,12/360,0.7714452897529168,0.999994660071484,0,0,0.8315548108298791,0.2157797618908021,RBL2;DLG1;FZD2;PARD6A;LAMA2;PXN;SPP1;H2-Q4;EIF2AK2;PPP2R5D;TCIRG1;PPP2R3A
+KEGG_2019_Mouse,FoxO signaling pathway,4/132,0.77379552923939,0.999994660071484,0,0,0.7546677215189873,0.1935327368496253,RBL2;TGFB1;SGK1;PRKAB1
+KEGG_2019_Mouse,"Alanine, aspartate and glutamate metabolism",1/37,0.7769204373512978,0.999994660071484,0,0,0.6715006305170239,0.1694983969795005,GFPT1
+KEGG_2019_Mouse,Huntington disease,6/192,0.7785574336046389,0.999994660071484,0,0,0.7786147044375307,0.1948970054508973,COX8B;DLG4;POLR2B;NDUFA4;IFT57;AP2A2
+KEGG_2019_Mouse,Arrhythmogenic right ventricular cardiomyopathy (ARVC),2/72,0.7853579710904967,0.999994660071484,0,0,0.6903318903318904,0.1667949890986456,LAMA2;CACNG5
+KEGG_2019_Mouse,B cell receptor signaling pathway,2/72,0.7853579710904967,0.999994660071484,0,0,0.6903318903318904,0.1667949890986456,INPPL1;NFKBIB
+KEGG_2019_Mouse,Bile secretion,2/72,0.7853579710904967,0.999994660071484,0,0,0.6903318903318904,0.1667949890986456,ADCY3;ABCB1A
+KEGG_2019_Mouse,Aldosterone-regulated sodium reabsorption,1/38,0.785793049845434,0.999994660071484,0,0,0.6533178828260795,0.1574899956403992,SGK1
+KEGG_2019_Mouse,Inositol phosphate metabolism,2/73,0.7917572586609677,0.999994660071484,0,0,0.6805733390240433,0.1589141644063754,INPPL1;PLCG1
+KEGG_2019_Mouse,African trypanosomiasis,1/39,0.7943131928270415,0.999994660071484,0,0,0.6360921218557112,0.1464776693086864,IL12A
+KEGG_2019_Mouse,Amoebiasis,3/106,0.797467591175662,0.999994660071484,0,0,0.7034109459575565,0.1591918033283303,TGFB1;LAMA2;IL12A
+KEGG_2019_Mouse,Bacterial invasion of epithelial cells,2/74,0.7979875521465601,0.999994660071484,0,0,0.6710858585858586,0.1514387652381934,PXN;HCLS1
+KEGG_2019_Mouse,Ferroptosis,1/40,0.8024948513857095,0.999994660071484,0,0,0.6197497332427976,0.1363634344702453,SLC40A1
+KEGG_2019_Mouse,"Parathyroid hormone synthesis, secretion and action",3/107,0.8026263639959617,0.999994660071484,0,0,0.6966109112126811,0.1531610361101095,MMP17;ADCY3;PLD1
+KEGG_2019_Mouse,Pancreatic cancer,2/75,0.8040521635143886,0.999994660071484,0,0,0.6618583091185831,0.1443454278028647,TGFB1;PLD1
+KEGG_2019_Mouse,Pertussis,2/76,0.8099544085577488,0.999994660071484,0,0,0.6528801528801529,0.137612328011429,IRF8;IL12A
+KEGG_2019_Mouse,Porphyrin and chlorophyll metabolism,1/41,0.8103514563677723,0.999994660071484,0,0,0.6042244640605297,0.1270606880426848,ALAD
+KEGG_2019_Mouse,Thermogenesis,7/231,0.8153961825172849,0.999994660071484,0,0,0.7537325285895807,0.1538226165564065,KLB;COA3;CPT1A;COX8B;NDUFA4;ADCY3;PRKAB1
+KEGG_2019_Mouse,Cardiac muscle contraction,2/78,0.8212850478463981,0.999994660071484,0,0,0.6356326422115895,0.1251465542926389,COX8B;CACNG5
+KEGG_2019_Mouse,Salmonella infection,2/78,0.8212850478463981,0.999994660071484,0,0,0.6356326422115895,0.1251465542926389,CCL3;LBP
+KEGG_2019_Mouse,Systemic lupus erythematosus,4/143,0.8241917241439921,0.999994660071484,0,0,0.6945451233949549,0.1342917589902741,HIST1H2BJ;HIST2H3C2;TRIM21;HIST1H2BA
+KEGG_2019_Mouse,Nucleotide excision repair,1/43,0.8251405883943322,0.999994660071484,0,0,0.5753918212934607,0.1105911694087926,LIG1
+KEGG_2019_Mouse,Parkinson disease,4/144,0.8282758015349989,0.999994660071484,0,0,0.6895479204339964,0.1299170937310528,COX8B;ADORA2A;NDUFA4;LRRK2
+KEGG_2019_Mouse,Hedgehog signaling pathway,1/44,0.8320973993299161,0.999994660071484,0,0,0.5619812897738936,0.1032954084751544,BTRC
+KEGG_2019_Mouse,Leukocyte transendothelial migration,3/115,0.8400369507873777,0.999994660071484,0,0,0.6465820841610981,0.1127053345458044,PXN;PLCG1;RAPGEF4
+KEGG_2019_Mouse,Endocytosis,8/269,0.8420080565102842,0.999994660071484,0,0,0.7387909098885672,0.1270466933747296,EHD2;PARD6A;GRK5;H2-Q4;AGAP1;PLD1;AP2A2;SPG21
+KEGG_2019_Mouse,ECM-receptor interaction,2/83,0.8470008029436239,0.999994660071484,0,0,0.5962401795735129,0.0990078499532633,LAMA2;SPP1
+KEGG_2019_Mouse,RNA degradation,2/83,0.8470008029436239,0.999994660071484,0,0,0.5962401795735129,0.0990078499532633,LSM7;LSM5
+KEGG_2019_Mouse,Phospholipase D signaling pathway,4/149,0.8475294838001037,0.999994660071484,0,0,0.6655958096900917,0.1101092826292228,ADCY3;PLCG1;PLD1;RAPGEF4
+KEGG_2019_Mouse,Peroxisome,2/84,0.8517222944985142,0.999994660071484,0,0,0.5889381621088938,0.0945214835105986,PECR;SLC25A17
+KEGG_2019_Mouse,Non-alcoholic fatty liver disease (NAFLD),4/151,0.8547046130193011,0.999994660071484,0,0,0.656471196073366,0.1030655521188968,TGFB1;COX8B;NDUFA4;PRKAB1
+KEGG_2019_Mouse,Transcriptional misregulation in cancer,5/183,0.8563768802440082,0.999994660071484,0,0,0.6774326768345652,0.1050323590731219,NR4A3;RARA;HIST2H3C2;PLAT;AFF1
+KEGG_2019_Mouse,ABC transporters,1/48,0.8572677404245432,0.999994660071484,0,0,0.5140457728528883,0.0791656157928406,ABCB1A
+KEGG_2019_Mouse,Tryptophan metabolism,1/48,0.8572677404245432,0.999994660071484,0,0,0.5140457728528883,0.0791656157928406,DLD
+KEGG_2019_Mouse,Insulin secretion,2/86,0.8607689802493782,0.999994660071484,0,0,0.5748556998556998,0.0861876127587135,ADCY3;RAPGEF4
+KEGG_2019_Mouse,Neurotrophin signaling pathway,3/121,0.8639216943031445,0.999994660071484,0,0,0.6135121815337804,0.0897403568280067,PRDM4;PLCG1;NFKBIB
+KEGG_2019_Mouse,mTOR signaling pathway,4/154,0.8649282169130451,0.999994660071484,0,0,0.6432405063291139,0.0933398333625276,FZD2;RRAGD;SGK1;TELO2
+KEGG_2019_Mouse,Viral myocarditis,2/87,0.8651003634451919,0.999994660071484,0,0,0.5680629827688651,0.0823178657697126,LAMA2;H2-Q4
+KEGG_2019_Mouse,Fatty acid degradation,1/50,0.8684020418177673,0.999994660071484,0,0,0.4930128419589777,0.0695643534483578,CPT1A
+KEGG_2019_Mouse,Linoleic acid metabolism,1/50,0.8684020418177673,0.999994660071484,0,0,0.4930128419589777,0.0695643534483578,PLA2G6
+KEGG_2019_Mouse,Calcium signaling pathway,5/189,0.8744851293034686,0.999994660071484,0,0,0.655135835124263,0.0878668113062166,ADORA2A;ADCY3;PLCG1;CACNA1H;DRD5
+KEGG_2019_Mouse,Sphingolipid signaling pathway,3/124,0.8746450339745783,0.999994660071484,0,0,0.5982071026318814,0.0801221546699894,PPP2R5D;PPP2R3A;PLD1
+KEGG_2019_Mouse,GnRH signaling pathway,2/90,0.8773629908528048,0.999994660071484,0,0,0.5486111111111112,0.0717772448431637,ADCY3;PLD1
+KEGG_2019_Mouse,Progesterone-mediated oocyte maturation,2/90,0.8773629908528048,0.999994660071484,0,0,0.5486111111111112,0.0717772448431637,MOS;ADCY3
+KEGG_2019_Mouse,IL-17 signaling pathway,2/91,0.8812165381472914,0.999994660071484,0,0,0.5424185676994666,0.068589856567908,TRAF3IP2;TRAF4
+KEGG_2019_Mouse,TGF-beta signaling pathway,2/91,0.8812165381472914,0.999994660071484,0,0,0.5424185676994666,0.068589856567908,TGFB1;BMPR1B
+KEGG_2019_Mouse,Wnt signaling pathway,4/160,0.8835350255051094,0.999994660071484,0,0,0.6183057448880234,0.0765613032354832,FZD2;DAAM1;GPC4;BTRC
+KEGG_2019_Mouse,Human cytomegalovirus infection,7/255,0.8839742474646106,0.999994660071484,0,0,0.679929909415092,0.0838539529594157,GNAO1;PXN;H2-Q4;CCL3;ADCY3;GNG12;TAPBP
+KEGG_2019_Mouse,Inflammatory mediator regulation of TRP channels,3/127,0.8846130732782835,0.999994660071484,0,0,0.5836425920639452,0.071557462011637,ADCY3;PLCG1;PLA2G6
+KEGG_2019_Mouse,Small cell lung cancer,2/92,0.8849579008583652,0.999994660071484,0,0,0.5363636363636364,0.0655517916425217,TRAF4;LAMA2
+KEGG_2019_Mouse,Endocrine and other factor-regulated calcium reabsorption,1/55,0.8925884203658613,0.999994660071484,0,0,0.4472467423287095,0.0508205130662456,AP2A2
+KEGG_2019_Mouse,"Valine, leucine and isoleucine degradation",1/56,0.896864345623378,0.999994660071484,0,0,0.4390920554854981,0.0477954598423355,DLD
+KEGG_2019_Mouse,Spliceosome,3/132,0.8996624032423824,0.999994660071484,0,0,0.5608737835533473,0.0593043784905716,LSM7;LSM5;CRNKL1
+KEGG_2019_Mouse,Ovarian steroidogenesis,1/57,0.9009702536041344,0.999994660071484,0,0,0.4312286074581156,0.0449698287349791,ADCY3
+KEGG_2019_Mouse,Influenza A,4/168,0.9048493258444028,0.999994660071484,0,0,0.5878974992281568,0.0587820131464605,EIF2AK2;IL12A;AGFG1;NFKBIB
+KEGG_2019_Mouse,Pyrimidine metabolism,1/58,0.9049128964621872,0.999994660071484,0,0,0.4236410698878343,0.0423287697812107,DCTD
+KEGG_2019_Mouse,Estrogen signaling pathway,3/134,0.9051701870163528,0.999994660071484,0,0,0.5522529217050598,0.0550222293289658,GNAO1;RARA;ADCY3
+KEGG_2019_Mouse,Oxidative phosphorylation,3/134,0.9051701870163528,0.999994660071484,0,0,0.5522529217050598,0.0550222293289658,COX8B;NDUFA4;TCIRG1
+KEGG_2019_Mouse,Phosphatidylinositol signaling system,2/98,0.905204796300897,0.999994660071484,0,0,0.5026830808080808,0.0500642522426471,INPPL1;PLCG1
+KEGG_2019_Mouse,Hepatocellular carcinoma,4/171,0.911906185124888,0.999994660071484,0,0,0.577245508982036,0.0532325195070072,TGFB1;FZD2;TXNRD1;PLCG1
+KEGG_2019_Mouse,Signaling pathways regulating pluripotency of stem cells,3/137,0.912920166743514,0.999994660071484,0,0,0.5398041398569731,0.0491798509116197,FZD2;PCGF2;BMPR1B
+KEGG_2019_Mouse,Aldosterone synthesis and secretion,2/102,0.9167981085973476,0.999994660071484,0,0,0.4824747474747475,0.0419116144685036,ADCY3;CACNA1H
+KEGG_2019_Mouse,Glucagon signaling pathway,2/102,0.9167981085973476,0.999994660071484,0,0,0.4824747474747475,0.0419116144685036,CPT1A;PRKAB1
+KEGG_2019_Mouse,Longevity regulating pathway,2/102,0.9167981085973476,0.999994660071484,0,0,0.4824747474747475,0.0419116144685036,ADCY3;PRKAB1
+KEGG_2019_Mouse,NF-kappa B signaling pathway,2/102,0.9167981085973476,0.999994660071484,0,0,0.4824747474747475,0.0419116144685036,PLCG1;LBP
+KEGG_2019_Mouse,Vascular smooth muscle contraction,3/140,0.9200901618264052,0.999994660071484,0,0,0.52790056013362,0.0439654652957896,ADORA2A;ADCY3;PLA2G6
+KEGG_2019_Mouse,Alzheimer disease,4/175,0.920589266910598,0.999994660071484,0,0,0.5636242504996669,0.0466350068416539,ADAM17;NCSTN;COX8B;NDUFA4
+KEGG_2019_Mouse,Apoptosis,3/141,0.9223577071736624,0.999994660071484,0,0,0.5240477106579453,0.0423546689303458,PARP3;PARP4;CASP2
+KEGG_2019_Mouse,Basal cell carcinoma,1/63,0.9223952853228587,0.999994660071484,0,0,0.3893747711833381,0.0314542474561826,FZD2
+KEGG_2019_Mouse,Mitophagy,1/63,0.9223952853228587,0.999994660071484,0,0,0.3893747711833381,0.0314542474561826,RHOT1
+KEGG_2019_Mouse,Pancreatic secretion,2/105,0.9246043789066708,0.999994660071484,0,0,0.4683485338825144,0.03671352845105,RAB3D;ADCY3
+KEGG_2019_Mouse,Central carbon metabolism in cancer,1/64,0.925485849363174,0.999994660071484,0,0,0.3831742028463339,0.0296716449445979,SLC1A5
+KEGG_2019_Mouse,Glutathione metabolism,1/64,0.925485849363174,0.999994660071484,0,0,0.3831742028463339,0.0296716449445979,GSR
+KEGG_2019_Mouse,Graft-versus-host disease,1/64,0.925485849363174,0.999994660071484,0,0,0.3831742028463339,0.0296716449445979,H2-Q4
+KEGG_2019_Mouse,Tuberculosis,4/178,0.926589874793279,0.999994660071484,0,0,0.5538192928852029,0.0422255273880101,TGFB1;IL12A;TCIRG1;LBP
+KEGG_2019_Mouse,Measles,3/144,0.9288116240750004,0.999994660071484,0,0,0.5128170643139576,0.037871198418708,EIF2AK2;IL12A;NFKBIB
+KEGG_2019_Mouse,Aminoacyl-tRNA biosynthesis,1/66,0.931303056893734,0.999994660071484,0,0,0.3713454263265108,0.0264288534285473,PSTK
+KEGG_2019_Mouse,Non-small cell lung cancer,1/66,0.931303056893734,0.999994660071484,0,0,0.3713454263265108,0.0264288534285473,PLCG1
+KEGG_2019_Mouse,Glycolysis / Gluconeogenesis,1/67,0.934039274709356,0.999994660071484,0,0,0.3656998738965952,0.0249541860932317,DLD
+KEGG_2019_Mouse,Amphetamine addiction,1/68,0.9366666363166524,0.999994660071484,0,0,0.3602228454198114,0.0235686018748234,GRIN3A
+KEGG_2019_Mouse,Fc epsilon RI signaling pathway,1/68,0.9366666363166524,0.999994660071484,0,0,0.3602228454198114,0.0235686018748234,PLCG1
+KEGG_2019_Mouse,Renal cell carcinoma,1/68,0.9366666363166524,0.999994660071484,0,0,0.3602228454198114,0.0235686018748234,TGFB1
+KEGG_2019_Mouse,Acute myeloid leukemia,1/69,0.9391894670565032,0.999994660071484,0,0,0.3549069060158742,0.0222661653604898,RARA
+KEGG_2019_Mouse,Gastric cancer,3/150,0.9402643346688224,0.999994660071484,0,0,0.49173095281096,0.0302877924321121,TGFB1;FZD2;ABCB1A
+KEGG_2019_Mouse,Thyroid hormone signaling pathway,2/115,0.945920630191421,0.999994660071484,0,0,0.4266782872977563,0.0237218679907258,DIO3;PLCG1
+KEGG_2019_Mouse,Gastric acid secretion,1/74,0.9503754043961108,0.999994660071484,0,0,0.3305118416279431,0.0168224610967125,ADCY3
+KEGG_2019_Mouse,Epstein-Barr virus infection,5/229,0.952175372311086,0.999994660071484,0,0,0.5370156617780192,0.0263170145157973,PSMD13;H2-Q4;EIF2AK2;NFKBIB;TAPBP
+KEGG_2019_Mouse,Glioma,1/75,0.952352732064606,0.999994660071484,0,0,0.3260284243890801,0.0159166411495142,PLCG1
+KEGG_2019_Mouse,Chronic myeloid leukemia,1/76,0.9542513631223596,0.999994660071484,0,0,0.3216645649432534,0.0150629593541046,TGFB1
+KEGG_2019_Mouse,Renin secretion,1/76,0.9542513631223596,0.999994660071484,0,0,0.3216645649432534,0.0150629593541046,ACE
+KEGG_2019_Mouse,Ras signaling pathway,5/233,0.9568181925792916,0.999994660071484,0,0,0.5274831565606031,0.0232840992450028,RASA1;PLCG1;PLA2G6;GNG12;PLD1
+KEGG_2019_Mouse,Autoimmune thyroid disease,1/78,0.9578249239415636,0.999994660071484,0,0,0.3132768870473788,0.0134991854406021,H2-Q4
+KEGG_2019_Mouse,Salivary secretion,1/78,0.9578249239415636,0.999994660071484,0,0,0.3132768870473788,0.0134991854406021,ADCY3
+KEGG_2019_Mouse,Focal adhesion,4/199,0.95834038509844,0.999994660071484,0,0,0.4936319376825706,0.0210051526226603,DIAPH1;LAMA2;PXN;SPP1
+KEGG_2019_Mouse,Platelet activation,2/125,0.961418185735801,0.999994660071484,0,0,0.3917836905641784,0.0154150457928373,ADCY3;FERMT3
+KEGG_2019_Mouse,ErbB signaling pathway,1/84,0.9669578426537668,0.999994660071484,0,0,0.2905392060043452,0.0097622278701403,PLCG1
+KEGG_2019_Mouse,Ribosome,3/170,0.9672381027240664,0.999994660071484,0,0,0.4323868066647993,0.0144030577277247,MRPL14;MRPL15;MRPS6
+KEGG_2019_Mouse,Human T-cell leukemia virus 1 infection,5/245,0.9683839815514867,0.999994660071484,0,0,0.5007921419518377,0.0160887464257585,DLG1;TGFB1;H2-Q4;TSPO;ADCY3
+KEGG_2019_Mouse,Gap junction,1/86,0.9695398234980942,0.999994660071484,0,0,0.2836733180031155,0.0087750734917273,ADCY3
+KEGG_2019_Mouse,Colorectal cancer,1/88,0.9719202615848072,0.999994660071484,0,0,0.2771231030134365,0.0078928853423751,TGFB1
+KEGG_2019_Mouse,Complement and coagulation cascades,1/88,0.9719202615848072,0.999994660071484,0,0,0.2771231030134365,0.0078928853423751,PLAT
+KEGG_2019_Mouse,Necroptosis,3/176,0.9727581648668564,0.999994660071484,0,0,0.417259194843726,0.0115246044776015,PARP3;PARP4;EIF2AK2
+KEGG_2019_Mouse,Arachidonic acid metabolism,1/89,0.973039861531982,0.999994660071484,0,0,0.2739596469104666,0.0074873801543478,PLA2G6
+KEGG_2019_Mouse,Steroid hormone biosynthesis,1/89,0.973039861531982,0.999994660071484,0,0,0.2739596469104666,0.0074873801543478,CYP2D22
+KEGG_2019_Mouse,Purine metabolism,2/136,0.9735320494532615,0.999994660071484,0,0,0.3594150459822101,0.0096411407000535,ADCY3;PDE8B
+KEGG_2019_Mouse,Protein digestion and absorption,1/90,0.9741148703913364,0.999994660071484,0,0,0.2708672797086869,0.007103777614789,SLC1A5
+KEGG_2019_Mouse,Kaposi sarcoma-associated herpesvirus infection,4/216,0.974159313919616,0.999994660071484,0,0,0.4536422259374253,0.0118765449404524,H2-Q4;EIF2AK2;PLCG1;GNG12
+KEGG_2019_Mouse,PI3K-Akt signaling pathway,8/357,0.9744743596103924,0.999994660071484,0,0,0.5499391208614945,0.014219815283785,RBL2;LAMA2;YWHAB;SPP1;PPP2R5D;PPP2R3A;GNG12;SGK1
+KEGG_2019_Mouse,Retinol metabolism,1/91,0.9751470620719322,0.999994660071484,0,0,0.267843631778058,0.0067408170554638,ALDH1A2
+KEGG_2019_Mouse,Phagosome,3/180,0.975935402241498,0.999994660071484,0,0,0.4077438985193597,0.0099321850973809,COLEC12;H2-Q4;TCIRG1
+KEGG_2019_Mouse,Insulin signaling pathway,2/139,0.9761384099044492,0.999994660071484,0,0,0.3514893460148934,0.0084887802495064,INPPL1;PRKAB1
+KEGG_2019_Mouse,Cytokine-cytokine receptor interaction,6/292,0.9766203689430262,0.999994660071484,0,0,0.5037094884810621,0.0119163916603581,CCL25;TGFB1;CCL3;IL12A;BMPR1B;PF4
+KEGG_2019_Mouse,MAPK signaling pathway,6/294,0.9777813078692807,0.999994660071484,0,0,0.5001586294416244,0.0112381870527335,TGFB1;TAOK1;RASA1;GNG12;CACNA1H;CACNG5
+KEGG_2019_Mouse,Fluid shear stress and atherosclerosis,2/143,0.9792301983786712,0.999994660071484,0,0,0.3414463786804212,0.0071664568289376,PLAT;BMPR1B
+KEGG_2019_Mouse,Herpes simplex virus 1 infection,10/433,0.9793491804525208,0.999994660071484,0,0,0.5663808076422058,0.0118186850268043,ZFP764;ZFP605;ZFP97;H2-Q4;EIF2AK2;ZFP949;2610008E11RIK;IL12A;ZFP26;TAPBP
+KEGG_2019_Mouse,Prostate cancer,1/97,0.9805318456285708,0.999994660071484,0,0,0.2510245901639344,0.0049351823275144,PLAT
+KEGG_2019_Mouse,HIF-1 signaling pathway,1/104,0.9853593727508416,0.999994660071484,0,0,0.2338789652174977,0.0034494478617946,PLCG1
+KEGG_2019_Mouse,Hepatitis C,2/160,0.9885577258770644,0.999994660071484,0,0,0.3044367727912032,0.0035035316375416,YWHAB;EIF2AK2
+KEGG_2019_Mouse,C-type lectin receptor signaling pathway,1/112,0.989430177993062,0.999994660071484,0,0,0.2169319382434136,0.0023051359886229,IL12A
+KEGG_2019_Mouse,Hepatitis B,2/163,0.9897105480724842,0.999994660071484,0,0,0.2987169834995922,0.0030895563619781,TGFB1;YWHAB
+KEGG_2019_Mouse,Ribosome biogenesis in eukaryotes,1/115,0.9906460266240952,0.999994660071484,0,0,0.2111900179199575,0.0019847630550593,NOB1
+KEGG_2019_Mouse,Natural killer cell mediated cytotoxicity,1/118,0.9917221345822612,0.999994660071484,0,0,0.2057425550489863,0.0017101973795652,PLCG1
+KEGG_2019_Mouse,Cell adhesion molecules (CAMs),2/170,0.9919772114630284,0.999994660071484,0,0,0.2861652236652237,0.0023051021635767,H2-Q4;LRRC4C
+KEGG_2019_Mouse,cGMP-PKG signaling pathway,2/172,0.9925297125109196,0.999994660071484,0,0,0.2827688651218063,0.0021202942163906,ADCY3;ADRA2A
+KEGG_2019_Mouse,Osteoclast differentiation,1/128,0.9944925408651863,0.999994660071484,0,0,0.1894430598445055,0.0010462336067318,TGFB1
+KEGG_2019_Mouse,Breast cancer,1/147,0.9974614456924362,0.999994660071484,0,0,0.1646254037900119,0.00041844187163137073,FZD2
+KEGG_2019_Mouse,Pathways in cancer,10/535,0.9981427591113704,0.999994660071484,0,0,0.4538629737609329,0.0008437166079430285,FZD2;TGFB1;TRAF4;LAMA2;TXNRD1;RARA;ADCY3;IL12A;PLCG1;GNG12
+KEGG_2019_Mouse,JAK-STAT signaling pathway,1/164,0.998730543253102,0.999994660071484,0,0,0.1473243642609025,0.0001871407168639949,IL12A
+KEGG_2019_Mouse,RNA transport,1/167,0.998876630022096,0.999994660071484,0,0,0.1446390859782129,0.0001625745396043843,NUP214
+KEGG_2019_Mouse,MicroRNAs in cancer,3/281,0.9991372274938484,0.999994660071484,0,0,0.2582288151779461,0.0002228888870655566,PLCG1;ABCB1A;SLC7A1
+KEGG_2019_Mouse,NOD-like receptor signaling pathway,1/205,0.9997600808366974,0.999994660071484,0,0,0.1174616126400118,2.8184672981553727e-05,NFKBIB
+KEGG_2019_Mouse,Olfactory transduction,4/1133,0.999994660071484,0.999994660071484,0,0,0.0810709600744469,4.329142873905716e-07,OLFR46;ADCY3;OLFR1347;OLFR857

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/final_answer.txt

@@ -0,0 +1,37 @@
+**Plan update:**
+1. [✓] Explore input data directory and examine file contents
+2. [✓] Understand which file corresponds to which mouse model (5xFAD, 3xTG-AD, PS3O1S)
+3. [✓] Perform differential expression analysis for each model
+4. [✓] Run KEGG pathway enrichment on each model's DE results
+5. [✓] Compare and find shared pathways across all three models
+6. [✓] Write the output CSV to the required path
+
+**Final verification:**
+
+The output file has been successfully created at:
+`/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv`
+
+**Summary of analysis:**
+
+1. **Data processing:**
+   - **5xFAD model** (GSE168137): 10 5xFAD cortex samples vs 10 BL6 cortex samples - DESeq2 analysis identified 2,470 differentially expressed genes (p<0.05)
+   - **3xTG-AD model** (GSE161904): 3 3xTgAD cortex samples vs 3 WT cortex samples - DESeq2 analysis identified 2,018 differentially expressed genes (p<0.05)  
+   - **PS3O1S model**: Pre-computed DE results with 797 significant genes (p<0.05)
+
+2. **KEGG pathway enrichment:** Using gseapy with the KEGG_2019_Mouse database:
+   - 5xFAD: 294 enriched terms, 104 significant (p<0.05)
+   - 3xTG-AD: 285 enriched terms, 13 significant (p<0.05)
+   - PS3O1S: 253 enriched terms, 1 significant (p<0.05)
+
+3. **Shared pathways across all 3 models:** 241 common KEGG pathways identified
+
+4. **Output file** (`pathway_comparison.csv`): Contains 241 rows with columns `Pathway`, `5xFAD_pvalue`, `3xTG_AD_pvalue`, `PS3O1S_pvalue` - sorted by 5xFAD p-value.
+
+The solution is ready.
+
+<solution>
+The analysis is complete. The comparative pathway CSV file has been written to:
+`/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv`
+
+The file contains 241 shared KEGG pathways across all three Alzheimer's mouse models (5xFAD, 3xTG-AD, and PS3O1S) with their respective p-values from Fisher's exact test enrichment analysis. The Phagosome pathway (as shown in the benchmark example) is present with p-value 5.04e-10 for 5xFAD, 0.789 for 3xTG-AD, and 0.976 for PS3O1S, consistent with the example format.
+</solution>

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/output_validation.json

@@ -0,0 +1,15 @@
+{
+  "postprocess": [
+    {
+      "file": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv",
+      "actions": []
+    }
+  ],
+  "outputs": [
+    {
+      "path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv",
+      "exists": true,
+      "size_bytes": 20160
+    }
+  ]
+}

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv

@@ -0,0 +1,242 @@
+Pathway,5xFAD_pvalue,3xTG_AD_pvalue,PS3O1S_pvalue
+Osteoclast differentiation,1.4491959264547912e-19,0.0274651220046645,0.9944925408651863
+Chagas disease (American trypanosomiasis),1.3359970736741422e-16,0.9701958641756928,0.3889181271848207
+Epstein-Barr virus infection,1.6963168250434842e-14,0.2199017291138168,0.952175372311086
+Tuberculosis,4.9257016866932333e-14,0.9907455746093736,0.926589874793279
+Leishmaniasis,1.5442486922035986e-13,0.7970322741315354,0.5000717646329553
+Lysosome,4.606256207081136e-13,0.6731089415209385,0.2229203791880803
+Th17 cell differentiation,4.907465791685252e-13,0.4358752643314385,0.5807356024263919
+Influenza A,6.78550700709852e-13,0.7062232389103857,0.9048493258444028
+Pertussis,6.127998809193345e-11,0.986979645887388,0.8099544085577488
+Human T-cell leukemia virus 1 infection,1.4772745412328308e-10,0.3244871655018319,0.9683839815514867
+B cell receptor signaling pathway,3.624126834605312e-10,0.2197610871114105,0.7853579710904967
+Toxoplasmosis,3.6407735274443517e-10,0.8745413535046022,0.4284085297130833
+Phagosome,5.03709293625993e-10,0.7892466702042832,0.975935402241498
+NOD-like receptor signaling pathway,9.914743468875566e-10,0.9714852133528356,0.9997600808366974
+Toll-like receptor signaling pathway,1.731253545741801e-09,0.5466476171858378,0.5569928197426123
+Human immunodeficiency virus 1 infection,2.4362876207802884e-09,0.6409389686477108,0.6065418510564768
+Th1 and Th2 cell differentiation,3.226733362901804e-09,0.2636472256620824,0.6766847540983636
+Kaposi sarcoma-associated herpesvirus infection,7.621719050524273e-09,0.3746892680168481,0.974159313919616
+Chemokine signaling pathway,1.4637176414593147e-08,0.7225164376360633,0.5247063660060708
+Measles,2.09636997969844e-08,0.1811562861536059,0.9288116240750004
+Natural killer cell mediated cytotoxicity,2.1132113965807133e-08,0.8454812587331062,0.9917221345822612
+Antigen processing and presentation,3.368390420309606e-08,0.1851053651477905,0.4817694108602536
+Cytokine-cytokine receptor interaction,4.273095848805671e-08,0.8605260594211094,0.9766203689430262
+C-type lectin receptor signaling pathway,5.9194598211764906e-08,0.6865118733901385,0.989430177993062
+Rheumatoid arthritis,9.567173932568285e-08,0.5173852683965636,0.6534192208178323
+Apoptosis,1.0780081767274277e-07,0.8081118145904043,0.9223577071736624
+AGE-RAGE signaling pathway in diabetic complications,1.699539122742058e-07,0.9163657709710872,0.7699616525431697
+T cell receptor signaling pathway,1.699539122742058e-07,0.4241683529902816,0.7699616525431697
+NF-kappa B signaling pathway,2.175516924916217e-07,0.920406830283214,0.9167981085973476
+Human cytomegalovirus infection,2.204486466663897e-07,0.8791061907904815,0.8839742474646106
+Fc gamma R-mediated phagocytosis,2.208509143560335e-07,0.7093139681000211,0.2635116067051573
+Hepatitis C,4.937415170994045e-07,0.8365631150896315,0.9885577258770644
+Fc epsilon RI signaling pathway,9.793156591002146e-07,0.4682968362632194,0.9366666363166524
+Salmonella infection,1.0577365286207356e-06,0.8820111665972598,0.8212850478463981
+Inflammatory bowel disease (IBD),1.1102245111098808e-06,0.5187821895916216,0.6853320825488993
+Type I diabetes mellitus,1.3104366721833414e-06,0.1878396226097212,0.7651132084399163
+MAPK signaling pathway,2.9273622451389014e-06,0.333569440984493,0.9777813078692807
+Complement and coagulation cascades,3.629559051090015e-06,0.5677682258476782,0.9719202615848072
+Graft-versus-host disease,4.979888808940495e-06,0.5926127817954563,0.925485849363174
+HIF-1 signaling pathway,1.1264203432419908e-05,0.3245906502207875,0.9853593727508416
+Allograft rejection,1.4539416942254128e-05,0.5783456103378237,0.7196235651859002
+Cell adhesion molecules (CAMs),1.6287195140493054e-05,0.6151796023388979,0.9919772114630284
+JAK-STAT signaling pathway,1.6362580096637664e-05,0.9179567687842668,0.998730543253102
+Cholesterol metabolism,1.7077989262616993e-05,0.9131183035583184,0.3081479395235083
+Acute myeloid leukemia,1.85286354337813e-05,0.8154448094019996,0.9391894670565032
+Platelet activation,2.359171103875394e-05,0.6823966548485542,0.961418185735801
+Sphingolipid signaling pathway,5.229293554651837e-05,0.9916000325037968,0.8746450339745783
+Other glycan degradation,6.253277003109979e-05,0.4304326951276698,0.5178436354613557
+Hepatitis B,7.875760542371166e-05,0.5530424888717204,0.9897105480724842
+Autoimmune thyroid disease,0.0001355961618984,0.7612097177361434,0.9578249239415636
+Intestinal immune network for IgA production,0.0002035029236132,0.4578346242892255,0.5136186434315607
+Human papillomavirus infection,0.00024136223839,0.3004200590292384,0.7714452897529168
+Viral myocarditis,0.0002549774464299,0.3996830194326157,0.8651003634451919
+Fluid shear stress and atherosclerosis,0.0003380629576225,0.7214002512058846,0.9792301983786712
+Glycosphingolipid biosynthesis,0.0003473463561669,0.2931989731782813,0.5381607227996654
+Progesterone-mediated oocyte maturation,0.0004259112160011,0.7396077523861038,0.8773629908528048
+Leukocyte transendothelial migration,0.0005015861890562,0.9082062657843176,0.8400369507873777
+PI3K-Akt signaling pathway,0.0005639797264782,0.1711679454240514,0.9744743596103924
+Legionellosis,0.0006136078694919,0.9922892552731756,0.6762387905131497
+Necroptosis,0.0007988157492484,0.9075798555982664,0.9727581648668564
+Relaxin signaling pathway,0.0008081866706115,0.9097715094532048,0.5987375750491811
+Insulin signaling pathway,0.0009889710588372,0.5663342238622817,0.9761384099044492
+Cellular senescence,0.0010243834495922,0.6322367281320294,0.7478192115948229
+Phospholipase D signaling pathway,0.0014841565404682,0.5403048367439922,0.8475294838001037
+Calcium signaling pathway,0.0015204342567552,0.4517782270638859,0.8744851293034686
+Proteoglycans in cancer,0.0015919807236426,0.4662464839581345,0.7000727022670568
+VEGF signaling pathway,0.001836789605335,0.1819685781510032,0.6762387905131497
+Herpes simplex virus 1 infection,0.0019174991389802,0.0633935586137539,0.9793491804525208
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+alpha-Linolenic acid metabolism,0.7987736748102128,0.1365485483216893,0.2613631306402563
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+N-Glycan biosynthesis,0.9832150388678954,0.9849040300405923,0.5956879709979176
+Fanconi anemia pathway,0.9848874586737985,0.0191701987843978,0.330102729560645
+Peroxisome,0.9901632869178488,0.9138077833080348,0.8517222944985142
+Nucleotide excision repair,0.9947339768794412,0.9728302680218608,0.8251405883943322
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+Basal cell carcinoma,0.9957977321790968,0.5783456103378237,0.9223952853228587
+Spliceosome,0.9984202321582688,0.8414655964686881,0.8996624032423824
+Huntington disease,0.9991448278826572,0.9752388351047568,0.7785574336046389
+Oxidative phosphorylation,0.9996332149186756,0.9989471412915992,0.9051701870163528
+Thermogenesis,0.9998873331375302,0.9362674202388386,0.8153961825172849
+RNA transport,0.9999812928723458,0.9272647911692458,0.998876630022096
+Ribosome biogenesis in eukaryotes,0.9999839024194264,0.9592008164763244,0.9906460266240952
+Olfactory transduction,0.9999930122614734,0.999993572132612,0.999994660071484
+Ribosome,0.9999959073849164,0.9995797841330062,0.9672381027240664

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/retrieval_plan.json

@@ -0,0 +1,509 @@
+{
+  "query": "You are running a bioagent-bench task with local files already prepared.\n\nTask ID: alzheimer-mouse\nTask name: Alzheimer Mouse Models: Comparative Pathway Analysis\nBenchmark prompt:\nPerform a comparative differential expression analysis of three different Alzheimer's Disease mouse models (5xFAD, 3xTG-AD, and PS3O1S) to identify shared molecular KEGG pathways. The output should be a CSV file with the following columns: 'pathway','5xFAD_pvalue','3xTG_AD_pvalue','PS3O1S_pvalue'. Example csv <example>Pathway,5xFAD_pvalue,3xTG_AD_pvalue,PS3O1S_pvalue\nPhagosome Homo sapiens hsa04145,1.5045916403148935e-09,0.3102788532065793,0.4443015705596512\n</example> \nData background:\nAnalyze 5xFAD, 3xTG-AD, and PS301S mouse models: normalize counts, perform differential expression, run KEGG pathway enrichment, and compare shared pathways across models.\n\nConstraints:\n1. Use only the benchmark inputs and references explicitly listed below.\n2. Save the required final deliverables exactly to the paths listed below.\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\n5. Return a concise final summary after writing the required files.\n\nTask-specific instruction:\nUse the provided mouse count and DEA files as inputs. Report the shared/comparative KEGG pathway set supported by the three model analyses, with the requested pathway and p-value columns.\n\nBenchmark data policy:\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/alzheimer-mouse/data\n- Allowed reference directory: <none>\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/alzheimer-mouse/results\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than alzheimer-mouse>\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\n- Do not download external databases or install new packages during the benchmark run.\n\nInput data directory:\n/225040511/project/bioagent-bench/dataset/alzheimer-mouse/data\nVisible input files:\n- DEA_PS3O1S.csv\n- GSE161904_Raw_gene_counts_cortex.txt\n- GSE168137_countList.txt\n\nReference data directory:\n<none>\nVisible reference files:\n- <none>\n\nRequired final output paths:\n- pathway_comparison.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv",
+  "query_context": {},
+  "mcp_config": "/225040511/project/Biomni/experiments/mcp_tool_scaling/configs/mcp_scale_100.yaml",
+  "planning_context_text": "{\"prompt\": \"You are running a bioagent-bench task with local files already prepared.\\n\\nTask ID: alzheimer-mouse\\nTask name: Alzheimer Mouse Models: Comparative Pathway Analysis\\nBenchmark prompt:\\nPerform a comparative differential expression analysis of three different Alzheimer's Disease mouse models (5xFAD, 3xTG-AD, and PS3O1S) to identify shared molecular KEGG pathways. The output should be a CSV file with the following columns: 'pathway','5xFAD_pvalue','3xTG_AD_pvalue','PS3O1S_pvalue'. Example csv <example>Pathway,5xFAD_pvalue,3xTG_AD_pvalue,PS3O1S_pvalue\\nPhagosome Homo sapiens hsa04145,1.5045916403148935e-09,0.3102788532065793,0.4443015705596512\\n</example> \\nData background:\\nAnalyze 5xFAD, 3xTG-AD, and PS301S mouse models: normalize counts, perform differential expression, run KEGG pathway enrichment, and compare shared pathways across models.\\n\\nConstraints:\\n1. Use only the benchmark inputs and references explicitly listed below.\\n2. Save the required final deliverables exactly to the paths listed below.\\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442\\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\\n5. Return a concise final summary after writing the required files.\\n\\nTask-specific instruction:\\nUse the provided mouse count and DEA files as inputs. Report the shared/comparative KEGG pathway set supported by the three model analyses, with the requested pathway and p-value columns.\\n\\nBenchmark data policy:\\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/alzheimer-mouse/data\\n- Allowed reference directory: <none>\\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442\\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/alzheimer-mouse/results\\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than alzheimer-mouse>\\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\\n- Do not download external databases or install new packages during the benchmark run.\\n\\nInput data directory:\\n/225040511/project/bioagent-bench/dataset/alzheimer-mouse/data\\nVisible input files:\\n- DEA_PS3O1S.csv\\n- GSE161904_Raw_gene_counts_cortex.txt\\n- GSE168137_countList.txt\\n\\nReference data directory:\\n<none>\\nVisible reference files:\\n- <none>\\n\\nRequired final output paths:\\n- pathway_comparison.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv\", \"selected_resources_names\": {\"tools\": [{\"description\": \"Executes the provided Python command in the notebook environment and returns the output.\", \"name\": \"run_python_repl\", \"optional_parameters\": [], \"required_parameters\": [{\"default\": null, \"description\": \"Python command to execute in the notebook environment\", \"name\": \"command\", \"type\": \"str\"}], \"id\": 174}, {\"name\": \"csvtk_headers\", \"description\": \"Print headers of a CSV/TSV file.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbeb1aa20>\", \"id\": 237}, {\"name\": \"csvtk_dim\", \"description\": \"Dimensions of CSV file (rows and columns).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1e40121120>\", \"id\": 238}, {\"name\": \"csvtk_cut\", \"description\": \"Select and arrange fields/columns.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbe3aa020>\", \"id\": 243}, {\"name\": \"csvtk_grep\", \"description\": \"Grep data by selected fields with patterns/regular expressions.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"pattern\": {\"type\": \"string\", \"description\": \"\"}, \"ignore_case\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"invert_match\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"use_regexp\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"pattern\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"ignore_case\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"invert_match\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"use_regexp\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbe3aa480>\", \"id\": 244}, {\"name\": \"csvtk_filter\", \"description\": \"Filter rows by values of selected fields with arithmetic expression (e.g., \\\"col1 > 10\\\").\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"filter_expr\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"filter_expr\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbe3aa5c0>\", \"id\": 245}, {\"name\": \"csvtk_sort\", \"description\": \"Sort by selected fields. Keys format: 1:n (numeric), 2:r (reverse).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"keys\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"keys\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbe3a9f80>\", \"id\": 247}, {\"name\": \"csvtk_join\", \"description\": \"Join files by selected fields.\", \"parameters\": {\"file1\": {\"type\": \"string\", \"description\": \"\"}, \"file2\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"left_join\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"outer_join\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"file1\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"file2\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"left_join\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"outer_join\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbe3aa520>\", \"id\": 248}, {\"name\": \"csvtk_concat\", \"description\": \"Concatenate CSV/TSV files by rows.\", \"parameters\": {\"files\": {\"type\": \"array\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"files\", \"type\": \"array\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbe3aa700>\", \"id\": 249}, {\"name\": \"csvtk_rename\", \"description\": \"Rename column names with new names.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"names\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"names\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbe3aaa20>\", \"id\": 254}, {\"name\": \"csvtk_round\", \"description\": \"Round float to n decimal places.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"decimal_places\": {\"type\": \"number\", \"description\": \"\", \"default\": 2}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"decimal_places\", \"type\": \"number\", \"description\": \"\", \"default\": 2}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dbe3aab60>\", \"id\": 256}, {\"description\": \"Returns a list of supported databases for gene set enrichment analysis.\", \"name\": \"get_gene_set_enrichment_analysis_supported_database_list\", \"optional_parameters\": [], \"required_parameters\": [], \"id\": 82}, {\"description\": \"Perform enrichment analysis for a list of genes, with optional background gene set and plotting functionality.\", \"name\": \"gene_set_enrichment_analysis\", \"optional_parameters\": [{\"default\": 10, \"description\": \"Number of top pathways to return\", \"name\": \"top_k\", \"type\": \"int\"}, {\"default\": \"ontology\", \"description\": \"Database to use for enrichment analysis (e.g., pathway, transcription, ontology)\", \"name\": \"database\", \"type\": \"str\"}, {\"default\": null, \"description\": \"List of background genes to use for enrichment analysis\", \"name\": \"background_list\", \"type\": \"list\"}, {\"default\": false, \"description\": \"Generate a bar plot of the top K enrichment results\", \"name\": \"plot\", \"type\": \"bool\"}], \"required_parameters\": [{\"default\": null, \"description\": \"List of gene symbols to analyze\", \"name\": \"genes\", \"type\": \"list\"}], \"id\": 83}, {\"description\": \"Convert ENSEMBL gene IDs between different species using BioMart homology mapping. This function converts a list of ENSEMBL gene IDs from one species to their homologous counterparts in another species using the Ensembl BioMart database. The conversion is based on one-to-one ortholog mappings between species.\", \"name\": \"interspecies_gene_conversion\", \"optional_parameters\": [], \"required_parameters\": [{\"default\": null, \"description\": \"List of ENSEMBL gene IDs to convert (e.g., ['ENSG00000007372', 'ENSG00000181449'])\", \"name\": \"gene_list\", \"type\": \"list[str]\"}, {\"default\": null, \"description\": \"Source species name. Supported species: human, mouse, rat, zebrafish, fly, drosophila, worm, yeast, chicken, pig, cow, dog, macaque\", \"name\": \"source_species\", \"type\": \"str\"}, {\"default\": null, \"description\": \"Target species name. Same supported species as source_species\", \"name\": \"target_species\", \"type\": \"str\"}], \"id\": 91}, {\"description\": \"Take a natural language prompt and convert it to a structured KEGG API query.\", \"name\": \"query_kegg\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Direct KEGG endpoint to query\", \"default\": null}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about KEGG data\", \"default\": null}], \"id\": 182}, {\"description\": \"Query the Ensembl REST API using natural language or a direct endpoint.\", \"name\": \"query_ensembl\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Direct Ensembl endpoint or full URL\", \"default\": null}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about genomic data\", \"default\": null}], \"id\": 193}], \"data_lake\": [], \"libraries\": [\"gseapy\", \"pandas\", \"numpy\", \"scipy\", \"scikit-learn\", \"matplotlib\", \"seaborn\", \"statsmodels\", \"DESeq2\", \"edgeR\", \"limma\"], \"know_how\": []}}",
+  "planning_latency_seconds": 37.81579716876149,
+  "total_runtime_seconds": 3025.2402216382325,
+  "selected_resources": {
+    "tools": [
+      {
+        "name": "run_python_repl",
+        "module": "biomni.tool.support_tools",
+        "description": "Executes the provided Python command in the notebook environment and returns the output."
+      },
+      {
+        "name": "csvtk_headers",
+        "module": "mcp_servers.csvtk",
+        "description": "Print headers of a CSV/TSV file."
+      },
+      {
+        "name": "csvtk_dim",
+        "module": "mcp_servers.csvtk",
+        "description": "Dimensions of CSV file (rows and columns)."
+      },
+      {
+        "name": "csvtk_cut",
+        "module": "mcp_servers.csvtk",
+        "description": "Select and arrange fields/columns."
+      },
+      {
+        "name": "csvtk_grep",
+        "module": "mcp_servers.csvtk",
+        "description": "Grep data by selected fields with patterns/regular expressions."
+      },
+      {
+        "name": "csvtk_filter",
+        "module": "mcp_servers.csvtk",
+        "description": "Filter rows by values of selected fields with arithmetic expression (e.g., \"col1 > 10\")."
+      },
+      {
+        "name": "csvtk_sort",
+        "module": "mcp_servers.csvtk",
+        "description": "Sort by selected fields. Keys format: 1:n (numeric), 2:r (reverse)."
+      },
+      {
+        "name": "csvtk_join",
+        "module": "mcp_servers.csvtk",
+        "description": "Join files by selected fields."
+      },
+      {
+        "name": "csvtk_concat",
+        "module": "mcp_servers.csvtk",
+        "description": "Concatenate CSV/TSV files by rows."
+      },
+      {
+        "name": "csvtk_rename",
+        "module": "mcp_servers.csvtk",
+        "description": "Rename column names with new names."
+      },
+      {
+        "name": "csvtk_round",
+        "module": "mcp_servers.csvtk",
+        "description": "Round float to n decimal places."
+      },
+      {
+        "name": "get_gene_set_enrichment_analysis_supported_database_list",
+        "module": "biomni.tool.genomics",
+        "description": "Returns a list of supported databases for gene set enrichment analysis."
+      },
+      {
+        "name": "gene_set_enrichment_analysis",
+        "module": "biomni.tool.genomics",
+        "description": "Perform enrichment analysis for a list of genes, with optional background gene set and plotting functionality."
+      },
+      {
+        "name": "interspecies_gene_conversion",
+        "module": "biomni.tool.genomics",
+        "description": "Convert ENSEMBL gene IDs between different species using BioMart homology mapping. This function converts a list of ENSEMBL gene IDs from one species to their homologous counterparts in another species using the Ensembl BioMart database. The conversion is based on one-to-one ortholog mappings between species."
+      },
+      {
+        "name": "query_kegg",
+        "module": "biomni.tool.database",
+        "description": "Take a natural language prompt and convert it to a structured KEGG API query."
+      },
+      {
+        "name": "query_ensembl",
+        "module": "biomni.tool.database",
+        "description": "Query the Ensembl REST API using natural language or a direct endpoint."
+      }
+    ],
+    "data_lake": [],
+    "libraries": [
+      {
+        "name": "gseapy",
+        "description": "[Python Package] A Python wrapper for Gene Set Enrichment Analysis (GSEA) and visualization."
+      },
+      {
+        "name": "pandas",
+        "description": "[Python Package] A fast, powerful, and flexible data analysis and manipulation library for Python."
+      },
+      {
+        "name": "numpy",
+        "description": "[Python Package] The fundamental package for scientific computing with Python, providing support for arrays, matrices, and mathematical functions."
+      },
+      {
+        "name": "scipy",
+        "description": "[Python Package] A Python library for scientific and technical computing, including modules for optimization, linear algebra, integration, and statistics."
+      },
+      {
+        "name": "scikit-learn",
+        "description": "[Python Package] A machine learning library featuring various classification, regression, and clustering algorithms."
+      },
+      {
+        "name": "matplotlib",
+        "description": "[Python Package] A comprehensive library for creating static, animated, and interactive visualizations in Python."
+      },
+      {
+        "name": "seaborn",
+        "description": "[Python Package] A statistical data visualization library based on matplotlib with a high-level interface for drawing attractive statistical graphics."
+      },
+      {
+        "name": "statsmodels",
+        "description": "[Python Package] A Python module for statistical modeling and econometrics, including descriptive statistics and estimation of statistical models."
+      },
+      {
+        "name": "DESeq2",
+        "description": "[R Package] Differential gene expression analysis based on the negative binomial distribution. Use with subprocess.run(['Rscript', '-e', 'library(DESeq2); ...'])."
+      },
+      {
+        "name": "edgeR",
+        "description": "[R Package] Empirical Analysis of Digital Gene Expression Data in R, for differential expression analysis. Use with subprocess calls."
+      },
+      {
+        "name": "limma",
+        "description": "[R Package] Linear Models for Microarray Data, for differential expression analysis. Use with subprocess calls."
+      }
+    ],
+    "know_how": []
+  },
+  "selected_resource_names": {
+    "tools": [
+      "run_python_repl",
+      "csvtk_headers",
+      "csvtk_dim",
+      "csvtk_cut",
+      "csvtk_grep",
+      "csvtk_filter",
+      "csvtk_sort",
+      "csvtk_join",
+      "csvtk_concat",
+      "csvtk_rename",
+      "csvtk_round",
+      "get_gene_set_enrichment_analysis_supported_database_list",
+      "gene_set_enrichment_analysis",
+      "interspecies_gene_conversion",
+      "query_kegg",
+      "query_ensembl"
+    ],
+    "data_lake": [],
+    "libraries": [
+      "gseapy",
+      "pandas",
+      "numpy",
+      "scipy",
+      "scikit-learn",
+      "matplotlib",
+      "seaborn",
+      "statsmodels",
+      "DESeq2",
+      "edgeR",
+      "limma"
+    ],
+    "know_how": []
+  },
+  "registered_tool_count": 331,
+  "registered_tool_names": [
+    "fetch_supplementary_info_from_doi",
+    "query_arxiv",
+    "query_scholar",
+    "query_pubmed",
+    "search_google",
+    "extract_url_content",
+    "extract_pdf_content",
+    "advanced_web_search_claude",
+    "analyze_circular_dichroism_spectra",
+    "analyze_rna_secondary_structure_features",
+    "analyze_protease_kinetics",
+    "analyze_enzyme_kinetics_assay",
+    "analyze_itc_binding_thermodynamics",
+    "analyze_protein_conservation",
+    "split_modalities",
+    "prepare_input_for_nnunet",
+    "segment_with_nn_unet",
+    "create_segmentation_visualization",
+    "quick_rigid_registration",
+    "quick_affine_registration",
+    "quick_deformable_registration",
+    "batch_register_images",
+    "calculate_similarity_metrics",
+    "create_registration_visualization",
+    "analyze_cell_migration_metrics",
+    "perform_crispr_cas9_genome_editing",
+    "analyze_calcium_imaging_data",
+    "analyze_in_vitro_drug_release_kinetics",
+    "analyze_myofiber_morphology",
+    "decode_behavior_from_neural_trajectories",
+    "simulate_whole_cell_ode_model",
+    "predict_protein_disorder_regions",
+    "analyze_cell_morphology_and_cytoskeleton",
+    "analyze_tissue_deformation_flow",
+    "find_n_glycosylation_motifs",
+    "predict_o_glycosylation_hotspots",
+    "list_glycoengineering_resources",
+    "analyze_ddr_network_in_cancer",
+    "analyze_cell_senescence_and_apoptosis",
+    "detect_and_annotate_somatic_mutations",
+    "detect_and_characterize_structural_variations",
+    "perform_gene_expression_nmf_analysis",
+    "analyze_copy_number_purity_ploidy_and_focal_events",
+    "quantify_cell_cycle_phases_from_microscopy",
+    "quantify_and_cluster_cell_motility",
+    "perform_facs_cell_sorting",
+    "analyze_flow_cytometry_immunophenotyping",
+    "analyze_mitochondrial_morphology_and_potential",
+    "annotate_open_reading_frames",
+    "annotate_plasmid",
+    "get_gene_coding_sequence",
+    "get_plasmid_sequence",
+    "align_sequences",
+    "pcr_simple",
+    "digest_sequence",
+    "find_restriction_sites",
+    "find_restriction_enzymes",
+    "find_sequence_mutations",
+    "design_knockout_sgrna",
+    "get_oligo_annealing_protocol",
+    "get_golden_gate_assembly_protocol",
+    "get_bacterial_transformation_protocol",
+    "design_primer",
+    "design_verification_primers",
+    "design_golden_gate_oligos",
+    "golden_gate_assembly",
+    "liftover_coordinates",
+    "bayesian_finemapping_with_deep_vi",
+    "analyze_cas9_mutation_outcomes",
+    "analyze_crispr_genome_editing",
+    "simulate_demographic_history",
+    "identify_transcription_factor_binding_sites",
+    "fit_genomic_prediction_model",
+    "perform_pcr_and_gel_electrophoresis",
+    "analyze_protein_phylogeny",
+    "annotate_celltype_scRNA",
+    "annotate_celltype_with_panhumanpy",
+    "create_scvi_embeddings_scRNA",
+    "create_harmony_embeddings_scRNA",
+    "get_uce_embeddings_scRNA",
+    "map_to_ima_interpret_scRNA",
+    "get_rna_seq_archs4",
+    "get_gene_set_enrichment_analysis_supported_database_list",
+    "gene_set_enrichment_analysis",
+    "analyze_chromatin_interactions",
+    "analyze_comparative_genomics_and_haplotypes",
+    "perform_chipseq_peak_calling_with_macs2",
+    "find_enriched_motifs_with_homer",
+    "analyze_genomic_region_overlap",
+    "unsupervised_celltype_transfer_between_scRNA_datasets",
+    "generate_embeddings_with_state",
+    "interspecies_gene_conversion",
+    "generate_gene_embeddings_with_ESM_models",
+    "generate_transcriptformer_embeddings",
+    "analyze_atac_seq_differential_accessibility",
+    "analyze_bacterial_growth_curve",
+    "isolate_purify_immune_cells",
+    "estimate_cell_cycle_phase_durations",
+    "track_immune_cells_under_flow",
+    "analyze_cfse_cell_proliferation",
+    "analyze_cytokine_production_in_cd4_tcells",
+    "analyze_ebv_antibody_titers",
+    "analyze_cns_lesion_histology",
+    "analyze_immunohistochemistry_image",
+    "optimize_anaerobic_digestion_process",
+    "analyze_arsenic_speciation_hplc_icpms",
+    "count_bacterial_colonies",
+    "annotate_bacterial_genome",
+    "enumerate_bacterial_cfu_by_serial_dilution",
+    "model_bacterial_growth_dynamics",
+    "quantify_biofilm_biomass_crystal_violet",
+    "segment_and_analyze_microbial_cells",
+    "segment_cells_with_deep_learning",
+    "simulate_generalized_lotka_volterra_dynamics",
+    "predict_rna_secondary_structure",
+    "simulate_microbial_population_dynamics",
+    "analyze_aortic_diameter_and_geometry",
+    "analyze_atp_luminescence_assay",
+    "analyze_thrombus_histology",
+    "analyze_intracellular_calcium_with_rhod2",
+    "quantify_corneal_nerve_fibers",
+    "segment_and_quantify_cells_in_multiplexed_images",
+    "analyze_bone_microct_morphometry",
+    "run_diffdock_with_smiles",
+    "docking_autodock_vina",
+    "run_autosite",
+    "retrieve_topk_repurposing_drugs_from_disease_txgnn",
+    "predict_admet_properties",
+    "predict_binding_affinity_protein_1d_sequence",
+    "analyze_accelerated_stability_of_pharmaceutical_formulations",
+    "run_3d_chondrogenic_aggregate_assay",
+    "grade_adverse_events_using_vcog_ctcae",
+    "analyze_radiolabeled_antibody_biodistribution",
+    "estimate_alpha_particle_radiotherapy_dosimetry",
+    "perform_mwas_cyp2c19_metabolizer_status",
+    "calculate_physicochemical_properties",
+    "analyze_xenograft_tumor_growth_inhibition",
+    "analyze_pixel_distribution",
+    "find_roi_from_image",
+    "analyze_western_blot",
+    "query_drug_interactions",
+    "check_drug_combination_safety",
+    "analyze_interaction_mechanisms",
+    "find_alternative_drugs_ddinter",
+    "query_fda_adverse_events",
+    "get_fda_drug_label_info",
+    "check_fda_drug_recalls",
+    "analyze_fda_safety_signals",
+    "reconstruct_3d_face_from_mri",
+    "analyze_abr_waveform_p1_metrics",
+    "analyze_ciliary_beat_frequency",
+    "analyze_protein_colocalization",
+    "perform_cosinor_analysis",
+    "calculate_brain_adc_map",
+    "analyze_endolysosomal_calcium_dynamics",
+    "analyze_fatty_acid_composition_by_gc",
+    "analyze_hemodynamic_data",
+    "simulate_thyroid_hormone_pharmacokinetics",
+    "quantify_amyloid_beta_plaques",
+    "engineer_bacterial_genome_for_therapeutic_delivery",
+    "analyze_bacterial_growth_rate",
+    "analyze_barcode_sequencing_data",
+    "analyze_bifurcation_diagram",
+    "create_biochemical_network_sbml_model",
+    "optimize_codons_for_heterologous_expression",
+    "simulate_gene_circuit_with_growth_feedback",
+    "identify_fas_functional_domains",
+    "perform_flux_balance_analysis",
+    "model_protein_dimerization_network",
+    "simulate_metabolic_network_perturbation",
+    "simulate_protein_signaling_network",
+    "compare_protein_structures",
+    "simulate_renin_angiotensin_system_dynamics",
+    "query_chatnt",
+    "run_python_repl",
+    "read_function_source_code",
+    "download_synapse_data",
+    "query_uniprot",
+    "query_alphafold",
+    "query_interpro",
+    "query_pdb",
+    "query_pdb_identifiers",
+    "query_kegg",
+    "query_stringdb",
+    "query_iucn",
+    "query_paleobiology",
+    "query_jaspar",
+    "query_worms",
+    "query_cbioportal",
+    "query_clinvar",
+    "query_geo",
+    "query_dbsnp",
+    "query_ucsc",
+    "query_ensembl",
+    "query_opentarget",
+    "query_monarch",
+    "query_openfda",
+    "query_gwas_catalog",
+    "query_gnomad",
+    "blast_sequence",
+    "query_reactome",
+    "query_regulomedb",
+    "query_pride",
+    "query_gtopdb",
+    "query_remap",
+    "query_mpd",
+    "query_emdb",
+    "query_synapse",
+    "query_pubchem",
+    "query_chembl",
+    "query_unichem",
+    "query_clinicaltrials",
+    "query_dailymed",
+    "query_quickgo",
+    "query_encode",
+    "region_to_ccre_screen",
+    "get_genes_near_ccre",
+    "test_pylabrobot_script",
+    "get_pylabrobot_documentation_liquid",
+    "get_pylabrobot_documentation_material",
+    "search_protocols",
+    "get_protocol_details",
+    "list_local_protocols",
+    "read_local_protocol",
+    "kallisto_index",
+    "kallisto_quant",
+    "kallisto_bus",
+    "kallisto_quant_tcc",
+    "kallisto_h5dump",
+    "kallisto_inspect",
+    "kallisto_version",
+    "kallisto_cite",
+    "kallisto_bus_list_technologies",
+    "kallisto_merge",
+    "kraken2_classify",
+    "kraken2_build_db",
+    "kraken2_inspect_db",
+    "csvtk_headers",
+    "csvtk_dim",
+    "csvtk_ncol",
+    "csvtk_nrow",
+    "csvtk_corr",
+    "csvtk_summary",
+    "csvtk_cut",
+    "csvtk_grep",
+    "csvtk_filter",
+    "csvtk_filter2",
+    "csvtk_sort",
+    "csvtk_join",
+    "csvtk_concat",
+    "csvtk_uniq",
+    "csvtk_freq",
+    "csvtk_mutate",
+    "csvtk_mutate2",
+    "csvtk_rename",
+    "csvtk_replace",
+    "csvtk_round",
+    "csvtk_transpose",
+    "csvtk_sep",
+    "csvtk_gather",
+    "csvtk_spread",
+    "csvtk_pretty",
+    "csvtk_csv2md",
+    "csvtk_csv2json",
+    "csvtk_xlsx2csv",
+    "csvtk_fix",
+    "csvtk_fix_quotes",
+    "csvtk_del_quotes",
+    "csvtk_head",
+    "csvtk_sample",
+    "csvtk_split",
+    "csvtk_comb",
+    "csvtk_fmtdate",
+    "csvtk_fold",
+    "csvtk_unfold",
+    "csvtk_plot",
+    "csvtk_version",
+    "megahit_assemble",
+    "megahit_core_contig2fastg",
+    "kaiju_classify",
+    "kaiju_makedb",
+    "kaiju_mkbwt",
+    "kaiju_mkfmi",
+    "kaiju_multi_classify",
+    "kaiju2krona",
+    "kaiju2table",
+    "kaiju_add_taxon_names",
+    "kaiju_merge_outputs",
+    "kaijux_search",
+    "kaijup_search",
+    "fastp_tool",
+    "spades_py",
+    "metaspades_py",
+    "rnaspades_py",
+    "plasmidspades_py",
+    "metaviralspades_py",
+    "coronaspades_py",
+    "biosyntheticspades_py",
+    "spades_test",
+    "spades_kmercount",
+    "spades_hammer",
+    "settings",
+    "scanpy_filter",
+    "scanpy_norm",
+    "scanpy_log1p",
+    "scanpy_hvg",
+    "scanpy_scale",
+    "scanpy_pca",
+    "scanpy_neighbors",
+    "scanpy_umap",
+    "scanpy_tsne",
+    "scanpy_diffexp",
+    "scanpy_louvain",
+    "scanpy_leiden",
+    "scanpy_paga",
+    "scanpy_cli_read",
+    "scanpy_cli_filter",
+    "scanpy_cli_norm",
+    "scanpy_cli_hvg",
+    "scanpy_cli_scale",
+    "scanpy_cli_regress",
+    "scanpy_cli_pca",
+    "scanpy_cli_neighbor",
+    "scanpy_cli_embed",
+    "scanpy_cli_cluster",
+    "scanpy_cli_diffexp",
+    "scanpy_cli_paga",
+    "scanpy_cli_dpt",
+    "scanpy_cli_integrate",
+    "scanpy_cli_multiplet",
+    "scanpy_cli_plot"
+  ]
+}

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/run_metadata.json

@@ -0,0 +1,32 @@
+{
+  "task_id": "alzheimer-mouse",
+  "task_name": "Alzheimer Mouse Models: Comparative Pathway Analysis",
+  "run_dir": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442",
+  "dataset_dir": "/225040511/project/bioagent-bench/dataset/alzheimer-mouse",
+  "data_dir": "/225040511/project/bioagent-bench/dataset/alzheimer-mouse/data",
+  "reference_dir": "/225040511/project/bioagent-bench/dataset/alzheimer-mouse/reference",
+  "agent_runtime_dir": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/agent_runtime",
+  "output_paths": [
+    "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv"
+  ],
+  "mcp_config": "/225040511/project/Biomni/experiments/mcp_tool_scaling/configs/mcp_scale_100.yaml",
+  "agent_kwargs": {
+    "path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/agent_runtime",
+    "expected_data_lake_files": [],
+    "use_tool_retriever": true,
+    "timeout_seconds": 1200,
+    "llm": "deepseek-v4-flash",
+    "source": "Custom",
+    "base_url": "https://api.deepseek.com/v1",
+    "api_key": "sk-06e6154722b84e89b081b1c9571838ef"
+  },
+  "query": "You are running a bioagent-bench task with local files already prepared.\n\nTask ID: alzheimer-mouse\nTask name: Alzheimer Mouse Models: Comparative Pathway Analysis\nBenchmark prompt:\nPerform a comparative differential expression analysis of three different Alzheimer's Disease mouse models (5xFAD, 3xTG-AD, and PS3O1S) to identify shared molecular KEGG pathways. The output should be a CSV file with the following columns: 'pathway','5xFAD_pvalue','3xTG_AD_pvalue','PS3O1S_pvalue'. Example csv <example>Pathway,5xFAD_pvalue,3xTG_AD_pvalue,PS3O1S_pvalue\nPhagosome Homo sapiens hsa04145,1.5045916403148935e-09,0.3102788532065793,0.4443015705596512\n</example> \nData background:\nAnalyze 5xFAD, 3xTG-AD, and PS301S mouse models: normalize counts, perform differential expression, run KEGG pathway enrichment, and compare shared pathways across models.\n\nConstraints:\n1. Use only the benchmark inputs and references explicitly listed below.\n2. Save the required final deliverables exactly to the paths listed below.\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\n5. Return a concise final summary after writing the required files.\n\nTask-specific instruction:\nUse the provided mouse count and DEA files as inputs. Report the shared/comparative KEGG pathway set supported by the three model analyses, with the requested pathway and p-value columns.\n\nBenchmark data policy:\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/alzheimer-mouse/data\n- Allowed reference directory: <none>\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/alzheimer-mouse/results\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than alzheimer-mouse>\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\n- Do not download external databases or install new packages during the benchmark run.\n\nInput data directory:\n/225040511/project/bioagent-bench/dataset/alzheimer-mouse/data\nVisible input files:\n- DEA_PS3O1S.csv\n- GSE161904_Raw_gene_counts_cortex.txt\n- GSE168137_countList.txt\n\nReference data directory:\n<none>\nVisible reference files:\n- <none>\n\nRequired final output paths:\n- pathway_comparison.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv",
+  "timestamp_utc": "20260514_163442",
+  "runtime_environment": {
+    "execution_env_prefix": "/225040511/miniconda3/envs/biomni_e1",
+    "execution_python": "/225040511/miniconda3/envs/biomni_e1/bin/python",
+    "conda_default_env": "biomni_e1",
+    "conda_prefix": "/225040511/miniconda3/envs/biomni_e1"
+  },
+  "biomni_root": "/225040511/project/Biomni"
+}

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/run_summary.json

@@ -0,0 +1,17 @@
+{
+  "task_id": "alzheimer-mouse",
+  "run_dir": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442",
+  "outputs": [
+    {
+      "path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv",
+      "exists": true,
+      "size_bytes": 20160
+    }
+  ],
+  "planning_latency_seconds": 37.81579716876149,
+  "total_runtime_seconds": 3025.2402216382325,
+  "final_answer_path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/final_answer.txt",
+  "metadata_path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/run_metadata.json",
+  "retrieval_plan_path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/retrieval_plan.json",
+  "output_validation_path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/output_validation.json"
+}

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/sig_3xtg_genes.csv

@@ -0,0 +1,2019 @@
+gene_id
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Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/sig_ps3_genes.csv

@@ -0,0 +1,798 @@
+gene_id,gene_name
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+ENSMUSG00000038068,Rnf144b
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+ENSMUSG00000038267,Slc22a23
+ENSMUSG00000038291,Snx25
+ENSMUSG00000038319,Kcnh2
+ENSMUSG00000038526,Car14
+ENSMUSG00000038533,Cbfa2t2
+ENSMUSG00000038544,Inip
+ENSMUSG00000038615,Nfe2l1
+ENSMUSG00000038695,Josd2
+ENSMUSG00000038893,Fam117a
+ENSMUSG00000039069,Mtg2
+ENSMUSG00000039097,Rln1
+ENSMUSG00000039100,March6
+ENSMUSG00000039157,Fam102a
+ENSMUSG00000039201,Tbc1d25
+ENSMUSG00000039358,Drd5
+ENSMUSG00000039450,Dcxr
+ENSMUSG00000039477,Tnrc18
+ENSMUSG00000039488,Cntn5
+ENSMUSG00000039496,Cdnf
+ENSMUSG00000039579,Grin3a
+ENSMUSG00000039648,Ccbl1
+ENSMUSG00000039680,Mrps6
+ENSMUSG00000039684,Gm5422
+ENSMUSG00000039911,Spsb1
+ENSMUSG00000039989,Cbx4
+ENSMUSG00000040006,Ginm1
+ENSMUSG00000040270,Bach2
+ENSMUSG00000040373,Cacng5
+ENSMUSG00000040584,Abcb1a
+ENSMUSG00000040653,Ppp1r14c
+ENSMUSG00000040661,Rad54l2
+ENSMUSG00000040720,1110037F02Rik
+ENSMUSG00000040731,Eif4h
+ENSMUSG00000040859,Bsdc1
+ENSMUSG00000041073,Nacad
+ENSMUSG00000041124,Msantd4
+ENSMUSG00000041287,Sox15
+ENSMUSG00000041313,Slc7a1
+ENSMUSG00000041360,Pum3
+ENSMUSG00000041459,Tardbp
+ENSMUSG00000041515,Irf8
+ENSMUSG00000041623,D11Wsu47e
+ENSMUSG00000041736,Tspo
+ENSMUSG00000042050,Wdr60
+ENSMUSG00000042115,Klhdc8a
+ENSMUSG00000042155,Klhl23
+ENSMUSG00000042298,Ttc19
+ENSMUSG00000042417,Ccno
+ENSMUSG00000042425,Frmpd3
+ENSMUSG00000042558,Adprhl2
+ENSMUSG00000042628,Zfyve1
+ENSMUSG00000042632,Pla2g6
+ENSMUSG00000042655,Fam159b
+ENSMUSG00000042705,Commd10
+ENSMUSG00000042821,Snai1
+ENSMUSG00000042962,Gm5436
+ENSMUSG00000043099,Hic1
+ENSMUSG00000043154,Ppp2r3a
+ENSMUSG00000043223,Gm4835
+ENSMUSG00000043252,Tmem64
+ENSMUSG00000043644,0610009L18Rik
+ENSMUSG00000043668,Tox3
+ENSMUSG00000043670,Diras1
+ENSMUSG00000043794,D830025C05Rik
+ENSMUSG00000044098,Rsbn1
+ENSMUSG00000044145,1810024B03Rik
+ENSMUSG00000044177,Wfikkn2
+ENSMUSG00000044216,Kcnj4
+ENSMUSG00000044339,Alkbh2
+ENSMUSG00000044477,Zfand3
+ENSMUSG00000044519,Zfp488
+ENSMUSG00000044573,Acp1
+ENSMUSG00000044617,Zbtb39
+ENSMUSG00000044795,Cyb5d1
+ENSMUSG00000045404,Kcnk13
+ENSMUSG00000045532,C1ql1
+ENSMUSG00000045757,Zfp764
+ENSMUSG00000046287,Pnma3
+ENSMUSG00000046334,Gm6195
+ENSMUSG00000046613,Vwa5b2
+ENSMUSG00000046691,Chtf8
+ENSMUSG00000046717,Igbp1b
+ENSMUSG00000046962,Zbtb21
+ENSMUSG00000047061,Gm9817
+ENSMUSG00000047248,C2cd3
+ENSMUSG00000047368,Abhd17b
+ENSMUSG00000047515,BC049715
+ENSMUSG00000047606,Ankrd34c
+ENSMUSG00000047712,Ust
+ENSMUSG00000047767,Atg16l2
+ENSMUSG00000048100,Taf13
+ENSMUSG00000048485,Zbtb8b
+ENSMUSG00000049044,Rapgef4
+ENSMUSG00000049511,Htr1b
+ENSMUSG00000049658,Bdp1
+ENSMUSG00000049717,Lig4
+ENSMUSG00000049734,Trex1
+ENSMUSG00000049764,Zfp280b
+ENSMUSG00000050074,Spink8
+ENSMUSG00000050121,Opalin
+ENSMUSG00000050288,Fzd2
+ENSMUSG00000050587,Lrrc4c
+ENSMUSG00000050677,Ccdc96
+ENSMUSG00000050721,Plekho2
+ENSMUSG00000050799,Hist1h2ba
+ENSMUSG00000050896,Rtn4rl2
+ENSMUSG00000051113,Fam71e1
+ENSMUSG00000051185,Fam174a
+ENSMUSG00000051242,Pcdhb9
+ENSMUSG00000051396,Hspa14
+ENSMUSG00000051451,Crebzf
+ENSMUSG00000051675,Trim32
+ENSMUSG00000051950,B3glct
+ENSMUSG00000051951,Xkr4
+ENSMUSG00000052125,F730043M19Rik
+ENSMUSG00000052137,Rbm12b2
+ENSMUSG00000052188,Gm14964
+ENSMUSG00000052430,Bmpr1b
+ENSMUSG00000052496,Pkdrej
+ENSMUSG00000052593,Adam17
+ENSMUSG00000052850,Tas2r137
+ENSMUSG00000052915,Msl1
+ENSMUSG00000053181,A830005F24Rik
+ENSMUSG00000053510,Nrd1
+ENSMUSG00000053603,4930442H23Rik
+ENSMUSG00000053646,Plxnb1
+ENSMUSG00000053746,Ptrh1
+ENSMUSG00000053841,Txlna
+ENSMUSG00000054008,Ndst1
+ENSMUSG00000054493,Gm9947
+ENSMUSG00000054509,Parp4
+ENSMUSG00000054894,Atp5s
+ENSMUSG00000055013,Agap1
+ENSMUSG00000055210,Foxd2
+ENSMUSG00000055717,Slain1
+ENSMUSG00000055771,Gm7936
+ENSMUSG00000055917,Zfp277
+ENSMUSG00000056204,Pgpep1
+ENSMUSG00000056313,1810011O10Rik
+ENSMUSG00000056342,Usp34
+ENSMUSG00000056367,Actr3b
+ENSMUSG00000056394,Lig1
+ENSMUSG00000056938,Acbd4
+ENSMUSG00000056966,Gjc3
+ENSMUSG00000057176,Ccdc189
+ENSMUSG00000057182,Scn3a
+ENSMUSG00000058396,Gpr182
+ENSMUSG00000058441,Panx2
+ENSMUSG00000058503,Fam133b
+ENSMUSG00000058567,Gm2531
+ENSMUSG00000058586,Serhl
+ENSMUSG00000059395,Nkapl
+ENSMUSG00000059409,Ppp2r5d
+ENSMUSG00000059890,Ube4a
+ENSMUSG00000060149,BC002059
+ENSMUSG00000060187,Lrrc10
+ENSMUSG00000060301,2610008E11Rik
+ENSMUSG00000060530,A930017M01Rik
+ENSMUSG00000060716,Plekhh1
+ENSMUSG00000061740,Cyp2d22
+ENSMUSG00000062115,Rai1
+ENSMUSG00000062458,Gm8623
+ENSMUSG00000062545,Tlr12
+ENSMUSG00000062563,Cys1
+ENSMUSG00000062818,Vmn1r51
+ENSMUSG00000063108,Zfp26
+ENSMUSG00000063109,Dgkeos
+ENSMUSG00000063179,Pstk
+ENSMUSG00000063286,Gm8995
+ENSMUSG00000063388,BC023105
+ENSMUSG00000063543,Gm5616
+ENSMUSG00000063808,Gpatch1
+ENSMUSG00000063894,Zkscan8
+ENSMUSG00000067224,Gm3695
+ENSMUSG00000067321,Gm7931
+ENSMUSG00000068917,Clk2
+ENSMUSG00000069300,Hist1h2bj
+ENSMUSG00000069601,Ank3
+ENSMUSG00000069804,Gm10277
+ENSMUSG00000070047,Fat1
+ENSMUSG00000070056,Mfhas1
+ENSMUSG00000070305,Mpzl3
+ENSMUSG00000070576,Mn1
+ENSMUSG00000071035,Gm5499
+ENSMUSG00000071151,Gm4799
+ENSMUSG00000071176,Arhgef10
+ENSMUSG00000071253,Slc25a16
+ENSMUSG00000071341,Egr4
+ENSMUSG00000071414,Gm6736
+ENSMUSG00000071855,Ccdc112
+ENSMUSG00000072423,Psmb11
+ENSMUSG00000072676,Tmem254a
+ENSMUSG00000072889,Nfxl1
+ENSMUSG00000073226,Gm10482
+ENSMUSG00000073295,Nudt11
+ENSMUSG00000073374,C030034I22Rik
+ENSMUSG00000073486,Gm10518
+ENSMUSG00000073680,Tmem88b
+ENSMUSG00000073775,Kti12
+ENSMUSG00000074093,Svip
+ENSMUSG00000074272,Ceacam1
+ENSMUSG00000074364,Ehd2
+ENSMUSG00000074466,Gm15417
+ENSMUSG00000074649,BC029722
+ENSMUSG00000074734,4933416C03Rik
+ENSMUSG00000074749,Kiz
+ENSMUSG00000074811,Hps6
+ENSMUSG00000074890,Lcmt2
+ENSMUSG00000074892,B3galt5
+ENSMUSG00000074896,Ifit3
+ENSMUSG00000074930,Gm13981
+ENSMUSG00000075330,A930003A15Rik
+ENSMUSG00000075569,Rsph10b
+ENSMUSG00000075707,Dio3
+ENSMUSG00000076928,Trac
+ENSMUSG00000078190,Dnm3os
+ENSMUSG00000078247,Airn
+ENSMUSG00000078365,Mos
+ENSMUSG00000078441,Scamp4
+ENSMUSG00000078484,Klhl17
+ENSMUSG00000078584,AU022252
+ENSMUSG00000078695,Cisd3
+ENSMUSG00000078919,Dpm1
+ENSMUSG00000079450,Cldn34c1
+ENSMUSG00000079610,Ankrd39
+ENSMUSG00000079657,Rab26
+ENSMUSG00000079737,3110001I22Rik
+ENSMUSG00000079834,Tmlhe
+ENSMUSG00000080824,Gm9001
+ENSMUSG00000080839,Gm11625
+ENSMUSG00000080994,Gm13464
+ENSMUSG00000081003,Gm14301
+ENSMUSG00000081058,Hist2h3c2
+ENSMUSG00000081123,Gm11469
+ENSMUSG00000081265,Gm11282
+ENSMUSG00000081303,Gm16011
+ENSMUSG00000081651,Gm15530
+ENSMUSG00000081819,Gm12722
+ENSMUSG00000082114,Gm13489
+ENSMUSG00000082149,Gm13002
+ENSMUSG00000082193,Rpl5-ps1
+ENSMUSG00000082195,Gm13034
+ENSMUSG00000082379,Gm13884
+ENSMUSG00000082429,Gm13171
+ENSMUSG00000082507,Gm9378
+ENSMUSG00000082530,Gm12168
+ENSMUSG00000082588,Gm15443
+ENSMUSG00000082705,Gm15616
+ENSMUSG00000082718,Gm14928
+ENSMUSG00000082746,Rps12-ps1
+ENSMUSG00000083022,Rps15a-ps6
+ENSMUSG00000083411,Rpl30-ps10
+ENSMUSG00000083505,Gm7541
+ENSMUSG00000083679,Gm12892
+ENSMUSG00000083732,Gm14197
+ENSMUSG00000083761,Pgam1-ps1
+ENSMUSG00000083863,Gm13341
+ENSMUSG00000083985,Gm12468
+ENSMUSG00000084269,Gm14784
+ENSMUSG00000084378,Gm15238
+ENSMUSG00000085382,Gm13861
+ENSMUSG00000085642,3110053B16Rik
+ENSMUSG00000085975,Gm13572
+ENSMUSG00000086098,Gm14291
+ENSMUSG00000086123,Gm16060
+ENSMUSG00000086308,G630016G05Rik
+ENSMUSG00000086350,B230369F24Rik
+ENSMUSG00000086468,Etaa1os
+ENSMUSG00000086688,Gm11560
+ENSMUSG00000086914,Gm16124
+ENSMUSG00000086968,4933431E20Rik
+ENSMUSG00000086980,Gm13791
+ENSMUSG00000087301,Gm13629
+ENSMUSG00000087331,1810021B22Rik
+ENSMUSG00000087993,Mir1982
+ENSMUSG00000089696,Gm4778
+ENSMUSG00000089837,Npcd
+ENSMUSG00000090002,Gm16006
+ENSMUSG00000090110,Cmc4
+ENSMUSG00000090386,Mir99ahg
+ENSMUSG00000090551,A730015C16Rik
+ENSMUSG00000090576,Gm17055
+ENSMUSG00000090812,Samd15
+ENSMUSG00000091058,Gm17538
+ENSMUSG00000091154,Proscos
+ENSMUSG00000091269,Gm6682
+ENSMUSG00000091387,Gcnt4
+ENSMUSG00000091625,Lsm5
+ENSMUSG00000092062,Gm7664
+ENSMUSG00000092083,Kcnb2
+ENSMUSG00000092181,Gm20432
+ENSMUSG00000092428,Gm20545
+ENSMUSG00000092448,Gm20387
+ENSMUSG00000092519,Actl9
+ENSMUSG00000093483,AA465934
+ENSMUSG00000093656,Gm20628
+ENSMUSG00000093661,Eif4e3
+ENSMUSG00000093730,Gm20690
+ENSMUSG00000093942,Olfr46
+ENSMUSG00000094002,Gm9866
+ENSMUSG00000094519,Gm9048
+ENSMUSG00000094566,Gm12620
+ENSMUSG00000094678,Olfr857
+ENSMUSG00000094828,Trav3-3
+ENSMUSG00000095139,Pou3f2
+ENSMUSG00000095512,Gm17222
+ENSMUSG00000095990,Zfp97
+ENSMUSG00000096789,Gm10257
+ENSMUSG00000096847,Tmem151b
+ENSMUSG00000096923,A730071L15Rik
+ENSMUSG00000096929,A330023F24Rik
+ENSMUSG00000097061,9330151L19Rik
+ENSMUSG00000097248,Gm2694
+ENSMUSG00000097320,Tmem147os
+ENSMUSG00000097403,9230116N13Rik
+ENSMUSG00000097511,Gm16677
+ENSMUSG00000097573,G730003C15Rik
+ENSMUSG00000097714,Gm20109
+ENSMUSG00000097867,Lppos
+ENSMUSG00000097882,0610038B21Rik
+ENSMUSG00000097908,4933404O12Rik
+ENSMUSG00000097937,Gm6967
+ENSMUSG00000097977,Gm5652
+ENSMUSG00000098292,Gm27194
+ENSMUSG00000098713,Rps2-ps5
+ENSMUSG00000098975,Gm27177
+ENSMUSG00000099076,Mir7070
+ENSMUSG00000099492,Gm5525
+ENSMUSG00000099631,Gm8641
+ENSMUSG00000099757,BE692007
+ENSMUSG00000099930,Gm2396
+ENSMUSG00000100000,1700023F02Rik
+ENSMUSG00000100075,1700018L02Rik
+ENSMUSG00000100162,Gm20687
+ENSMUSG00000100432,Gm7539
+ENSMUSG00000100441,Gm7266
+ENSMUSG00000100600,A230077H06Rik
+ENSMUSG00000100636,Gm3551
+ENSMUSG00000100671,Gm28322
+ENSMUSG00000100922,Gm8520
+ENSMUSG00000101330,Gm10193
+ENSMUSG00000101462,Gm3052
+ENSMUSG00000101567,Txn-ps1
+ENSMUSG00000101578,Vmn1r206
+ENSMUSG00000101587,Gm29036
+ENSMUSG00000101609,Kcnq1ot1
+ENSMUSG00000101610,Gm7560
+ENSMUSG00000101784,Gm7553
+ENSMUSG00000102151,Gm37472
+ENSMUSG00000102306,Gm38193
+ENSMUSG00000102344,9430053O09Rik
+ENSMUSG00000102404,5530400K19Rik
+ENSMUSG00000102526,Gm37785
+ENSMUSG00000102536,1700039I01Rik
+ENSMUSG00000102579,Gm37965
+ENSMUSG00000102649,Gm38021
+ENSMUSG00000102665,Gm38379
+ENSMUSG00000102748,Pcdhgb2
+ENSMUSG00000102776,Gm38162
+ENSMUSG00000103007,Gm20690
+ENSMUSG00000103044,Gm37307
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+ENSMUSG00000103310,Pcdha12
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+ENSMUSG00000103459,Gm38096
+ENSMUSG00000103529,A730089K16Rik
+ENSMUSG00000103583,Gm38325
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+ENSMUSG00000103677,Pcdhga4
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+ENSMUSG00000103957,Gm10766
+ENSMUSG00000104026,Gm37212
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+ENSMUSG00000104467,Gm37660
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+ENSMUSG00000104563,Gm43041
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+ENSMUSG00000104802,Gm5869
+ENSMUSG00000104822,Gm42967
+ENSMUSG00000105084,Gm43365
+ENSMUSG00000105113,Gm2622
+ENSMUSG00000105305,Gm8872
+ENSMUSG00000105345,BC030343
+ENSMUSG00000105403,Gm43618
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+ENSMUSG00000105861,Gm43508
+ENSMUSG00000105892,Gm35013
+ENSMUSG00000105941,Gm42809
+ENSMUSG00000105970,Gm43360
+ENSMUSG00000105993,Gm43337
+ENSMUSG00000106044,Gm42860
+ENSMUSG00000106189,Gm42933
+ENSMUSG00000106262,Gm43375
+ENSMUSG00000106427,Gm42820
+ENSMUSG00000106515,Gm30382
+ENSMUSG00000106735,A330058E17Rik
+ENSMUSG00000106747,Gm43025
+ENSMUSG00000106818,Gm43790
+ENSMUSG00000106868,Gm19798
+ENSMUSG00000106992,Gm43167
+ENSMUSG00000107035,Ybx1-ps2
+ENSMUSG00000107219,Gm42738
+ENSMUSG00000107760,Gm44401
+ENSMUSG00000107909,Gm44143
+ENSMUSG00000108053,Gm43890
+ENSMUSG00000108105,Gm5340
+ENSMUSG00000108199,Gm44249
+ENSMUSG00000108211,Gm44130
+ENSMUSG00000108291,Gm44292
+ENSMUSG00000108297,Gm44167
+ENSMUSG00000108443,Gm44510
+ENSMUSG00000108551,Gm20274
+ENSMUSG00000108658,Gm45138
+ENSMUSG00000108776,Gm45169
+ENSMUSG00000108804,Gm44628
+ENSMUSG00000108892,Gm9449
+ENSMUSG00000109044,Gm44680
+ENSMUSG00000109108,Gm44890
+ENSMUSG00000109118,Gm45109
+ENSMUSG00000109206,Gm45137
+ENSMUSG00000109231,RP23-323L3.1
+ENSMUSG00000109427,RP23-423B21.1
+ENSMUSG00000109549,Gm38941
+ENSMUSG00000109636,RP24-367H14.3
+ENSMUSG00000109679,RP23-322E23.5
+ENSMUSG00000109710,RP23-182C11.3
+ENSMUSG00000109865,Hspa14
+ENSMUSG00000109887,RP24-571A14.6
+ENSMUSG00000109946,RP23-328F3.4
+ENSMUSG00000110030,RP23-423E20.7
+ENSMUSG00000110044,RP23-283I2.2
+ENSMUSG00000110191,Olfr1347
+ENSMUSG00000110236,RP24-371M20.1

Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/symbols_3xtg.txt

@@ -0,0 +1,1607 @@
+Klf6
+Ccnd2
+Cdh1
+Bcl6b
+Dbt
+Ccm2
+Tom1l2
+Dynlt1c
+Il12rb1
+Slc5a5
+Hip1r
+Myg1
+Rnd2
+Tubb6
+Gramd2b
+Folh1
+Spa17
+Fam98a
+Kmt2a
+Ccne1
+Clgn
+Smg9
+Peg3
+Zim1
+Rec8
+Borcs8
+Snx9
+Nr2f6
+Ocel1
+Zfp40
+Top3a
+Timmdc1
+Map2k7
+Timm44
+Klf4
+Grk5
+St8sia6
+Inmt
+Cyp4f18
+Prodh
+Fosb
+Homer3
+Ciao1
+Nfat5
+Gpx6
+Pde1c
+Utp20
+Col26a1
+Coro1c
+Ctse
+Ndrg2
+Pnpla6
+Mcoln1
+Arhgef18
+Stxbp2
+Wwox
+Crabp2
+Eif2ak4
+Igf1r
+Fcer2a
+Insrr
+Cd247
+Bloc1s6
+Cnga2
+Apc
+Sra1
+Eps8l1
+Susd2
+Fbln1
+Slc4a1
+Chrd
+Scube2
+Rad51c
+Ccdc124
+Crlf1
+Fzd3
+Emc10
+Cers4
+Rdh13
+Apobec3
+Fxyd5
+Pou6f1
+Cacna2d2
+Rassf1
+Rnf112
+Slc3a2
+Tsacc
+Cfap161
+Odc1
+Rpa3
+Aldh1a2
+Atraid
+Chp1
+Trpv4
+Klra17
+Wdfy2
+Stc1
+Ptgds
+Map2
+Abca1
+Arnt
+Serac1
+Arnt2
+Prune1
+Tnr
+Rxrg
+Fcrl2
+Celsr1
+Tenm1
+Hsd11b1
+Lonrf3
+Ppfibp1
+H3f3b
+Phf21b
+Ift27
+Etaa1
+Brca1
+Taok1
+Rarb
+Rhbdl3
+Cadps2
+Med13l
+Ro60
+Smurf2
+Kif3a
+6330403K07Rik
+Slc13a3
+Slc2a4
+Glra2
+Dusp14
+Tada2a
+Rps6kl1
+Xab2
+Pdk4
+Slc35e1
+Calr3
+Tmc4
+Iyd
+Vip
+Fbxo5
+Lama4
+Arfgef3
+Fam184a
+Lrriq1
+Slc17a8
+Epyc
+Dusp6
+Psen1
+Sgk1
+Il20ra
+Igf1
+Srgn
+Hkdc1
+Slc16a7
+Lgr5
+Ptprb
+Meis1
+Adora2a
+Mdm1
+Rufy1
+Kremen1
+Upp1
+Pttg1
+Btg2
+Gabra6
+Zkscan17
+Aebp1
+2810021J22Rik
+Tubd1
+Mfap3
+Fam114a2
+Acaca
+Pctp
+Lpin1
+Rgs9
+Lratd1
+Dus4l
+Klf11
+Pxdn
+Hnf1b
+Helz
+Unk
+Exoc7
+Tekt1
+Pimreg
+Asgr1
+Alox8
+Per1
+Usp43
+Rcvrn
+Krt12
+Eftud2
+Klhl28
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Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/symbols_5xfad.txt

@@ -0,0 +1,2295 @@
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Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/symbols_ps3.txt

@@ -0,0 +1,794 @@
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Biomni/experiments/bioagent_bench/runs/scale_100/alzheimer-mouse_20260514_163442/task_query.txt

@@ -0,0 +1,44 @@
+You are running a bioagent-bench task with local files already prepared.
+
+Task ID: alzheimer-mouse
+Task name: Alzheimer Mouse Models: Comparative Pathway Analysis
+Benchmark prompt:
+Perform a comparative differential expression analysis of three different Alzheimer's Disease mouse models (5xFAD, 3xTG-AD, and PS3O1S) to identify shared molecular KEGG pathways. The output should be a CSV file with the following columns: 'pathway','5xFAD_pvalue','3xTG_AD_pvalue','PS3O1S_pvalue'. Example csv <example>Pathway,5xFAD_pvalue,3xTG_AD_pvalue,PS3O1S_pvalue
+Phagosome Homo sapiens hsa04145,1.5045916403148935e-09,0.3102788532065793,0.4443015705596512
+</example> 
+Data background:
+Analyze 5xFAD, 3xTG-AD, and PS301S mouse models: normalize counts, perform differential expression, run KEGG pathway enrichment, and compare shared pathways across models.
+
+Constraints:
+1. Use only the benchmark inputs and references explicitly listed below.
+2. Save the required final deliverables exactly to the paths listed below.
+3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442
+4. Keep final deliverables in the same schema/format requested by the benchmark prompt.
+5. Return a concise final summary after writing the required files.
+
+Task-specific instruction:
+Use the provided mouse count and DEA files as inputs. Report the shared/comparative KEGG pathway set supported by the three model analyses, with the requested pathway and p-value columns.
+
+Benchmark data policy:
+- Allowed input data directory: /225040511/project/bioagent-bench/dataset/alzheimer-mouse/data
+- Allowed reference directory: <none>
+- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442
+- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/alzheimer-mouse/results
+- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than alzheimer-mouse>
+- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.
+- Do not download external databases or install new packages during the benchmark run.
+
+Input data directory:
+/225040511/project/bioagent-bench/dataset/alzheimer-mouse/data
+Visible input files:
+- DEA_PS3O1S.csv
+- GSE161904_Raw_gene_counts_cortex.txt
+- GSE168137_countList.txt
+
+Reference data directory:
+<none>
+Visible reference files:
+- <none>
+
+Required final output paths:
+- pathway_comparison.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/alzheimer-mouse_20260514_163442/pathway_comparison.csv

Biomni/experiments/bioagent_bench/runs/scale_100/comparative-genomics_20260514_172511/final_answer.txt

@@ -0,0 +1,23 @@
+The file has been successfully generated. Let me provide the final summary.
+
+<solution>
+The task has been completed successfully. Here's a summary:
+
+**Task**: Comparative Genomics: Reconstruct phylogeny and identify COGs across four Micrococcus genomes; filter clusters present in all genomes, coding-only, with high-confidence annotations.
+
+**Steps performed**:
+1. **Data exploration**: Examined 5 Micrococcus genome FASTA files and their annotations
+   - Micrococcus sp. KBS0714, Micrococcus luteus SA211, Micrococcus luteus AS2, Micrococcus sp. KD337-16, Micrococcus yunnanensis TT9
+2. **Genome annotation**: Ran Prokka on all 5 genomes to obtain consistent protein annotations
+3. **Orthologous clustering**: 
+   - Ran all-vs-all BLASTP on all 11758 protein sequences
+   - Identified reciprocal best hits (37,954 pairs)
+   - Applied MCL clustering (inflation=2.0) to identify 2,342 orthologous clusters
+4. **Filtering**: Selected clusters present in all 5 genomes with non-hypothetical annotations → **1,124 core orthologous clusters**
+5. **Annotation**: Mapped KEGG KO numbers to core clusters where available (207 clusters have KEGG KO annotations)
+
+**Output file**: `/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/cluster_annotation_mapping.csv`
+- Format: `cluster_number,consensus_annotation`
+- 1,124 entries of evolutionarily conserved gene clusters
+- Each entry includes KEGG KO numbers, gene names, and functional descriptions in the required format
+</solution>

Biomni/experiments/bioagent_bench/runs/scale_100/comparative-genomics_20260514_172511/output_validation.json

@@ -0,0 +1,15 @@
+{
+  "postprocess": [
+    {
+      "file": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/cluster_annotation_mapping.csv",
+      "actions": []
+    }
+  ],
+  "outputs": [
+    {
+      "path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/cluster_annotation_mapping.csv",
+      "exists": true,
+      "size_bytes": 52250
+    }
+  ]
+}

Biomni/experiments/bioagent_bench/runs/scale_100/comparative-genomics_20260514_172511/retrieval_plan.json

@@ -0,0 +1,638 @@
+{
+  "query": "You are running a bioagent-bench task with local files already prepared.\n\nTask ID: comparative-genomics\nTask name: Comparative Genomics: Co-evolving Gene Clusters\nBenchmark prompt:\nReconstruct phylogeny and identify COGs across four Micrococcus genomes; filter clusters present in all genomes, coding-only, with high-confidence annotations. The output should be a CSV file with the following columns: 'cluster_number, 'consensus_annotation'.<example>cluster_number,consensus_annotation\n1,K07222  K07222, putative flavoprotein involved in K+ transport\n2,K01069  gloB, gloC, HAGH, hydroxyacylglutathione hydrolase [EC:3.1.2.6]\n</example>\nData background:\nThe datasets consists FASTA sequences and GFF annotations of a microbial genome for Micrococcus. The goal of is to do phylogenetic reconstruction of clusters of orthologous co-evolving genes; identify functionally conserved gene clusters across the genomes and group them into co-evolving functional modules.\n\nConstraints:\n1. Use only the benchmark inputs and references explicitly listed below.\n2. Save the required final deliverables exactly to the paths listed below.\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\n5. Return a concise final summary after writing the required files.\n\nBenchmark data policy:\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/data\n- Allowed reference directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/reference\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/results\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than comparative-genomics>\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\n- Do not download external databases or install new packages during the benchmark run.\n\nInput data directory:\n/225040511/project/bioagent-bench/dataset/comparative-genomics/data\nVisible input files:\n- GCF_002008305.4_ASM200830v4_genomic.fna\n- GCF_003691675.1_ASM369167v1_genomic.fna\n- GCF_005280335.1_ASM528033v1_genomic.fna\n- GCF_020097155.1_ASM2009715v1_genomic.fna\n- GCF_023573625.1_ASM2357362v1_genomic.fna\n- assembly_data_report.jsonl\n- genomic.gff\n\nReference data directory:\n/225040511/project/bioagent-bench/dataset/comparative-genomics/reference\nVisible reference files:\n- Actinobacteria.RData\n\nRequired final output paths:\n- cluster_annotation_mapping.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/cluster_annotation_mapping.csv",
+  "query_context": {},
+  "mcp_config": "/225040511/project/Biomni/experiments/mcp_tool_scaling/configs/mcp_scale_100.yaml",
+  "planning_context_text": "{\"prompt\": \"You are running a bioagent-bench task with local files already prepared.\\n\\nTask ID: comparative-genomics\\nTask name: Comparative Genomics: Co-evolving Gene Clusters\\nBenchmark prompt:\\nReconstruct phylogeny and identify COGs across four Micrococcus genomes; filter clusters present in all genomes, coding-only, with high-confidence annotations. The output should be a CSV file with the following columns: 'cluster_number, 'consensus_annotation'.<example>cluster_number,consensus_annotation\\n1,K07222  K07222, putative flavoprotein involved in K+ transport\\n2,K01069  gloB, gloC, HAGH, hydroxyacylglutathione hydrolase [EC:3.1.2.6]\\n</example>\\nData background:\\nThe datasets consists FASTA sequences and GFF annotations of a microbial genome for Micrococcus. The goal of is to do phylogenetic reconstruction of clusters of orthologous co-evolving genes; identify functionally conserved gene clusters across the genomes and group them into co-evolving functional modules.\\n\\nConstraints:\\n1. Use only the benchmark inputs and references explicitly listed below.\\n2. Save the required final deliverables exactly to the paths listed below.\\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511\\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\\n5. Return a concise final summary after writing the required files.\\n\\nBenchmark data policy:\\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/data\\n- Allowed reference directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/reference\\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511\\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/comparative-genomics/results\\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than comparative-genomics>\\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\\n- Do not download external databases or install new packages during the benchmark run.\\n\\nInput data directory:\\n/225040511/project/bioagent-bench/dataset/comparative-genomics/data\\nVisible input files:\\n- GCF_002008305.4_ASM200830v4_genomic.fna\\n- GCF_003691675.1_ASM369167v1_genomic.fna\\n- GCF_005280335.1_ASM528033v1_genomic.fna\\n- GCF_020097155.1_ASM2009715v1_genomic.fna\\n- GCF_023573625.1_ASM2357362v1_genomic.fna\\n- assembly_data_report.jsonl\\n- genomic.gff\\n\\nReference data directory:\\n/225040511/project/bioagent-bench/dataset/comparative-genomics/reference\\nVisible reference files:\\n- Actinobacteria.RData\\n\\nRequired final output paths:\\n- cluster_annotation_mapping.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/cluster_annotation_mapping.csv\", \"selected_resources_names\": {\"tools\": [{\"description\": \"Perform multiple sequence alignment and phylogenetic analysis to identify conserved protein regions.\", \"name\": \"analyze_protein_conservation\", \"optional_parameters\": [{\"default\": \"./\", \"description\": \"Directory to save output files\", \"name\": \"output_dir\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"List of protein sequences in FASTA format from multiple organisms.\", \"name\": \"protein_sequences\", \"type\": \"list of str\"}], \"id\": 13}, {\"description\": \"Perform phylogenetic analysis on a set of protein sequences. This function aligns sequences, constructs a phylogenetic tree, and visualizes evolutionary relationships.\", \"name\": \"analyze_protein_phylogeny\", \"optional_parameters\": [{\"default\": \"./\", \"description\": \"Directory to save output files\", \"name\": \"output_dir\", \"type\": \"str\"}, {\"default\": \"clustalw\", \"description\": \"Method for sequence alignment: \\\"clustalw\\\", \\\"muscle\\\", or \\\"pre-aligned\\\"\", \"name\": \"alignment_method\", \"type\": \"str\"}, {\"default\": \"fasttree\", \"description\": \"Method for tree construction: \\\"iqtree\\\" or fallback to neighbor-joining\", \"name\": \"tree_method\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Path to a FASTA file containing protein sequences or a string with FASTA-formatted sequences\", \"name\": \"fasta_sequences\", \"type\": \"str\"}], \"id\": 74}, {\"description\": \"Perform comparative genomics and haplotype analysis on multiple genome samples. Aligns genome samples to a reference, identifies variants, analyzes shared and unique genomic regions, and determines haplotype structure.\", \"name\": \"analyze_comparative_genomics_and_haplotypes\", \"optional_parameters\": [{\"default\": \"./output\", \"description\": \"Directory to store output files\", \"name\": \"output_dir\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Paths to FASTA files containing whole-genome sequences to be analyzed\", \"name\": \"sample_fasta_files\", \"type\": \"List[str]\"}, {\"default\": null, \"description\": \"Path to the reference genome FASTA file\", \"name\": \"reference_genome_path\", \"type\": \"str\"}], \"id\": 85}, {\"description\": \"Convert ENSEMBL gene IDs between different species using BioMart homology mapping. This function converts a list of ENSEMBL gene IDs from one species to their homologous counterparts in another species using the Ensembl BioMart database. The conversion is based on one-to-one ortholog mappings between species.\", \"name\": \"interspecies_gene_conversion\", \"optional_parameters\": [], \"required_parameters\": [{\"default\": null, \"description\": \"List of ENSEMBL gene IDs to convert (e.g., ['ENSG00000007372', 'ENSG00000181449'])\", \"name\": \"gene_list\", \"type\": \"list[str]\"}, {\"default\": null, \"description\": \"Source species name. Supported species: human, mouse, rat, zebrafish, fly, drosophila, worm, yeast, chicken, pig, cow, dog, macaque\", \"name\": \"source_species\", \"type\": \"str\"}, {\"default\": null, \"description\": \"Target species name. Same supported species as source_species\", \"name\": \"target_species\", \"type\": \"str\"}], \"id\": 91, \"module\": \"biomni.tool.genomics\"}, {\"description\": \"Annotate a bacterial genome using Prokka to identify genes, proteins, and functional features.\", \"name\": \"annotate_bacterial_genome\", \"optional_parameters\": [{\"default\": \"annotation_results\", \"description\": \"Directory where annotation results will be saved\", \"name\": \"output_dir\", \"type\": \"str\"}, {\"default\": \"\", \"description\": \"Genus name for the organism\", \"name\": \"genus\", \"type\": \"str\"}, {\"default\": \"\", \"description\": \"Species name for the organism\", \"name\": \"species\", \"type\": \"str\"}, {\"default\": \"\", \"description\": \"Strain identifier\", \"name\": \"strain\", \"type\": \"str\"}, {\"default\": \"\", \"description\": \"Prefix for output files\", \"name\": \"prefix\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Path to the assembled genome sequence file in FASTA format\", \"name\": \"genome_file_path\", \"type\": \"str\"}], \"id\": 107}, {\"description\": \"Executes the provided Python command in the notebook environment and returns the output.\", \"name\": \"run_python_repl\", \"optional_parameters\": [], \"required_parameters\": [{\"default\": null, \"description\": \"Python command to execute in the notebook environment\", \"name\": \"command\", \"type\": \"str\"}], \"id\": 174, \"module\": \"biomni.tool.support_tools\"}, {\"description\": \"Read the source code of a function from any module path.\", \"name\": \"read_function_source_code\", \"optional_parameters\": [], \"required_parameters\": [{\"default\": null, \"description\": \"Fully qualified function name (e.g., 'bioagentos.tool.support_tools.write_python_code')\", \"name\": \"function_name\", \"type\": \"str\"}], \"id\": 175}, {\"description\": \"Query the UniProt REST API using either natural language or a direct endpoint.\", \"name\": \"query_uniprot\", \"optional_parameters\": [{\"default\": null, \"description\": \"Full or partial UniProt API endpoint URL to query directly (e.g., 'https://rest.uniprot.org/uniprotkb/P01308')\", \"name\": \"endpoint\", \"type\": \"str\"}, {\"default\": 5, \"description\": \"Maximum number of results to return\", \"name\": \"max_results\", \"type\": \"int\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Natural language query about proteins (e.g., \\\"Find information about human insulin\\\")\", \"name\": \"prompt\", \"type\": \"str\"}], \"id\": 177}, {\"description\": \"Query the InterPro REST API using natural language or a direct endpoint.\", \"name\": \"query_interpro\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Endpoint path or full URL\", \"default\": null}, {\"name\": \"max_results\", \"type\": \"int\", \"description\": \"Max results per page\", \"default\": 3}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about protein domains/families\", \"default\": null}], \"id\": 179}, {\"description\": \"Take a natural language prompt and convert it to a structured KEGG API query.\", \"name\": \"query_kegg\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Direct KEGG endpoint to query\", \"default\": null}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about KEGG data\", \"default\": null}], \"id\": 182, \"module\": \"biomni.tool.database\"}, {\"description\": \"Query the STRING protein interaction database using natural language or direct endpoint.\", \"name\": \"query_stringdb\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Full URL to query directly\", \"default\": null}, {\"name\": \"download_image\", \"type\": \"bool\", \"description\": \"Download image results if endpoint is image\", \"default\": false}, {\"name\": \"output_dir\", \"type\": \"str\", \"description\": \"Directory to save downloaded files\", \"default\": null}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about protein interactions\", \"default\": null}], \"id\": 183}, {\"description\": \"Query the Ensembl REST API using natural language or a direct endpoint.\", \"name\": \"query_ensembl\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Direct Ensembl endpoint or full URL\", \"default\": null}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about genomic data\", \"default\": null}], \"id\": 193, \"module\": \"biomni.tool.database\"}, {\"description\": \"Identify a DNA or protein sequence using NCBI BLAST.\", \"name\": \"blast_sequence\", \"optional_parameters\": [], \"required_parameters\": [{\"name\": \"sequence\", \"type\": \"str\", \"description\": \"Query sequence\", \"default\": null}, {\"name\": \"database\", \"type\": \"str\", \"description\": \"BLAST database (e.g., core_nt or nr)\", \"default\": null}, {\"name\": \"program\", \"type\": \"str\", \"description\": \"BLAST program (blastn or blastp)\", \"default\": null}], \"id\": 199}, {\"description\": \"Query the Reactome database using natural language or a direct endpoint; optionally download pathway diagrams.\", \"name\": \"query_reactome\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Direct endpoint or full URL\", \"default\": null}, {\"name\": \"download\", \"type\": \"bool\", \"description\": \"Download pathway diagram if available\", \"default\": false}, {\"name\": \"output_dir\", \"type\": \"str\", \"description\": \"Directory to save downloads\", \"default\": null}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about biological pathways\", \"default\": null}], \"id\": 200}, {\"description\": \"Query the QuickGO API using natural language or a direct endpoint.\", \"name\": \"query_quickgo\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Direct QuickGO endpoint or full URL\", \"default\": null}, {\"name\": \"max_results\", \"type\": \"int\", \"description\": \"Max results (limit, up to 100)\", \"default\": 25}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about GO terms/annotations\", \"default\": null}], \"id\": 213}, {\"description\": \"Track immune cells under flow conditions and classify their behaviors.\", \"name\": \"track_immune_cells_under_flow\", \"optional_parameters\": [{\"default\": \"./output\", \"description\": \"Directory to save output files\", \"name\": \"output_dir\", \"type\": \"str\"}, {\"default\": 1.0, \"description\": \"Pixel size in micrometers\", \"name\": \"pixel_size_um\", \"type\": \"float\"}, {\"default\": 1.0, \"description\": \"Time interval between frames in seconds\", \"name\": \"time_interval_sec\", \"type\": \"float\"}, {\"default\": \"right\", \"description\": \"Direction of flow ('right', 'left', 'up', 'down')\", \"name\": \"flow_direction\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Path to image sequence directory or video file\", \"name\": \"image_sequence_path\", \"type\": \"str\"}], \"id\": 98}, {\"description\": \"Analyze cytokine production (IFN-γ, IL-17) in CD4+ T cells after antigen stimulation.\", \"name\": \"analyze_cytokine_production_in_cd4_tcells\", \"optional_parameters\": [{\"default\": \"./results\", \"description\": \"Directory to save the results file\", \"name\": \"output_dir\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Dictionary mapping stimulation conditions to FCS file paths. Expected keys: 'unstimulated', 'Mtb300', 'CMV', 'SEB'\", \"name\": \"fcs_files_dict\", \"type\": \"dict\"}], \"id\": 100}, {\"description\": \"Analyze ELISA data to quantify EBV antibody titers in plasma/serum samples.\", \"name\": \"analyze_ebv_antibody_titers\", \"optional_parameters\": [{\"default\": \"./\", \"description\": \"Directory to save output files.\", \"name\": \"output_dir\", \"type\": \"str\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Dictionary containing optical density (OD) readings for each sample. Format: {sample_id: {'VCA_IgG': float, 'VCA_IgM': float, 'EA_IgG': float, 'EA_IgM': float, 'EBNA1_IgG': float, 'EBNA1_IgM': float}}\", \"name\": \"raw_od_data\", \"type\": \"dict\"}, {\"default\": null, \"description\": \"Dictionary containing standard curve data for each antibody type. Format: {antibody_type: [(concentration, OD), ...]}\", \"name\": \"standard_curve_data\", \"type\": \"dict\"}, {\"default\": null, \"description\": \"Dictionary containing metadata for each sample. Format: {sample_id: {'group': str, 'collection_date': str}}\", \"name\": \"sample_metadata\", \"type\": \"dict\"}], \"id\": 101}, {\"description\": \"Analyzes arsenic speciation in liquid samples using HPLC-ICP-MS technique. Returns a research log summarizing analysis steps and results.\", \"name\": \"analyze_arsenic_speciation_hplc_icpms\", \"optional_parameters\": [{\"default\": \"Unknown Sample\", \"description\": \"Name of the sample being analyzed\", \"name\": \"sample_name\", \"type\": \"str\"}, {\"default\": null, \"description\": \"Dictionary containing calibration standards data with known concentrations for each arsenic species\", \"name\": \"calibration_data\", \"type\": \"dict\"}], \"required_parameters\": [{\"default\": null, \"description\": \"Dictionary containing sample data with keys as sample IDs and values as dictionaries with retention times (in minutes) as keys and signal intensities as values\", \"name\": \"sample_data\", \"type\": \"dict\"}], \"id\": 105}, {\"name\": \"csvtk_headers\", \"description\": \"Print headers of a CSV/TSV file.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac9049a0>\", \"id\": 237}, {\"name\": \"csvtk_dim\", \"description\": \"Dimensions of CSV file (rows and columns).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac904ae0>\", \"id\": 238}, {\"name\": \"csvtk_summary\", \"description\": \"Summary statistics of selected numeric or text fields (groupby group fields).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"groups\": {\"type\": \"string\", \"description\": \"\", \"default\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"groups\", \"type\": \"string\", \"description\": \"\", \"default\": \"\"}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac904cc0>\", \"id\": 242}, {\"name\": \"csvtk_cut\", \"description\": \"Select and arrange fields/columns.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac904540>\", \"id\": 243}, {\"name\": \"csvtk_grep\", \"description\": \"Grep data by selected fields with patterns/regular expressions.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"pattern\": {\"type\": \"string\", \"description\": \"\"}, \"ignore_case\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"invert_match\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"use_regexp\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"pattern\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"ignore_case\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"invert_match\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"use_regexp\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac904fe0>\", \"id\": 244}, {\"name\": \"csvtk_join\", \"description\": \"Join files by selected fields.\", \"parameters\": {\"file1\": {\"type\": \"string\", \"description\": \"\"}, \"file2\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"left_join\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"outer_join\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"file1\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"file2\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"left_join\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"outer_join\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac9047c0>\", \"id\": 248}, {\"name\": \"csvtk_concat\", \"description\": \"Concatenate CSV/TSV files by rows.\", \"parameters\": {\"files\": {\"type\": \"array\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"files\", \"type\": \"array\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac904a40>\", \"id\": 249}, {\"name\": \"csvtk_mutate\", \"description\": \"Create new column from selected fields by regular expression.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"name\": {\"type\": \"string\", \"description\": \"\"}, \"pattern\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"name\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"pattern\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac905760>\", \"id\": 252}, {\"name\": \"csvtk_rename\", \"description\": \"Rename column names with new names.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"names\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"names\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac905940>\", \"id\": 254}, {\"name\": \"csvtk_pretty\", \"description\": \"Convert CSV to a readable aligned table.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac905c60>\", \"id\": 261}, {\"name\": \"csvtk_csv2json\", \"description\": \"Convert CSV to JSON format.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1dac905da0>\", \"id\": 263}], \"data_lake\": [], \"libraries\": [\"biopython\", \"scikit-bio\", \"biotite\", \"gget\", \"pyfaidx\", \"pyranges\", \"pybedtools\", \"pandas\", \"numpy\", \"scipy\", \"scikit-learn\", \"matplotlib\", \"seaborn\", \"tqdm\", \"joblib\", \"ggplot2\", \"dplyr\", \"readr\", \"stringr\", \"Matrix\"], \"know_how\": []}}",
+  "planning_latency_seconds": 25.24140142649412,
+  "total_runtime_seconds": 6987.9035994187,
+  "selected_resources": {
+    "tools": [
+      {
+        "name": "analyze_protein_conservation",
+        "module": "biomni.tool.biochemistry",
+        "description": "Perform multiple sequence alignment and phylogenetic analysis to identify conserved protein regions."
+      },
+      {
+        "name": "analyze_protein_phylogeny",
+        "module": "biomni.tool.genetics",
+        "description": "Perform phylogenetic analysis on a set of protein sequences. This function aligns sequences, constructs a phylogenetic tree, and visualizes evolutionary relationships."
+      },
+      {
+        "name": "analyze_comparative_genomics_and_haplotypes",
+        "module": "biomni.tool.genomics",
+        "description": "Perform comparative genomics and haplotype analysis on multiple genome samples. Aligns genome samples to a reference, identifies variants, analyzes shared and unique genomic regions, and determines haplotype structure."
+      },
+      {
+        "name": "interspecies_gene_conversion",
+        "module": "biomni.tool.genomics",
+        "description": "Convert ENSEMBL gene IDs between different species using BioMart homology mapping. This function converts a list of ENSEMBL gene IDs from one species to their homologous counterparts in another species using the Ensembl BioMart database. The conversion is based on one-to-one ortholog mappings between species."
+      },
+      {
+        "name": "annotate_bacterial_genome",
+        "module": "biomni.tool.microbiology",
+        "description": "Annotate a bacterial genome using Prokka to identify genes, proteins, and functional features."
+      },
+      {
+        "name": "run_python_repl",
+        "module": "biomni.tool.support_tools",
+        "description": "Executes the provided Python command in the notebook environment and returns the output."
+      },
+      {
+        "name": "read_function_source_code",
+        "module": "biomni.tool.support_tools",
+        "description": "Read the source code of a function from any module path."
+      },
+      {
+        "name": "query_uniprot",
+        "module": "biomni.tool.database",
+        "description": "Query the UniProt REST API using either natural language or a direct endpoint."
+      },
+      {
+        "name": "query_interpro",
+        "module": "biomni.tool.database",
+        "description": "Query the InterPro REST API using natural language or a direct endpoint."
+      },
+      {
+        "name": "query_kegg",
+        "module": "biomni.tool.database",
+        "description": "Take a natural language prompt and convert it to a structured KEGG API query."
+      },
+      {
+        "name": "query_stringdb",
+        "module": "biomni.tool.database",
+        "description": "Query the STRING protein interaction database using natural language or direct endpoint."
+      },
+      {
+        "name": "query_ensembl",
+        "module": "biomni.tool.database",
+        "description": "Query the Ensembl REST API using natural language or a direct endpoint."
+      },
+      {
+        "name": "blast_sequence",
+        "module": "biomni.tool.database",
+        "description": "Identify a DNA or protein sequence using NCBI BLAST."
+      },
+      {
+        "name": "query_reactome",
+        "module": "biomni.tool.database",
+        "description": "Query the Reactome database using natural language or a direct endpoint; optionally download pathway diagrams."
+      },
+      {
+        "name": "query_quickgo",
+        "module": "biomni.tool.database",
+        "description": "Query the QuickGO API using natural language or a direct endpoint."
+      },
+      {
+        "name": "track_immune_cells_under_flow",
+        "module": "biomni.tool.immunology",
+        "description": "Track immune cells under flow conditions and classify their behaviors."
+      },
+      {
+        "name": "analyze_cytokine_production_in_cd4_tcells",
+        "module": "biomni.tool.immunology",
+        "description": "Analyze cytokine production (IFN-γ, IL-17) in CD4+ T cells after antigen stimulation."
+      },
+      {
+        "name": "analyze_ebv_antibody_titers",
+        "module": "biomni.tool.immunology",
+        "description": "Analyze ELISA data to quantify EBV antibody titers in plasma/serum samples."
+      },
+      {
+        "name": "analyze_arsenic_speciation_hplc_icpms",
+        "module": "biomni.tool.microbiology",
+        "description": "Analyzes arsenic speciation in liquid samples using HPLC-ICP-MS technique. Returns a research log summarizing analysis steps and results."
+      },
+      {
+        "name": "csvtk_headers",
+        "module": "mcp_servers.csvtk",
+        "description": "Print headers of a CSV/TSV file."
+      },
+      {
+        "name": "csvtk_dim",
+        "module": "mcp_servers.csvtk",
+        "description": "Dimensions of CSV file (rows and columns)."
+      },
+      {
+        "name": "csvtk_summary",
+        "module": "mcp_servers.csvtk",
+        "description": "Summary statistics of selected numeric or text fields (groupby group fields)."
+      },
+      {
+        "name": "csvtk_cut",
+        "module": "mcp_servers.csvtk",
+        "description": "Select and arrange fields/columns."
+      },
+      {
+        "name": "csvtk_grep",
+        "module": "mcp_servers.csvtk",
+        "description": "Grep data by selected fields with patterns/regular expressions."
+      },
+      {
+        "name": "csvtk_join",
+        "module": "mcp_servers.csvtk",
+        "description": "Join files by selected fields."
+      },
+      {
+        "name": "csvtk_concat",
+        "module": "mcp_servers.csvtk",
+        "description": "Concatenate CSV/TSV files by rows."
+      },
+      {
+        "name": "csvtk_mutate",
+        "module": "mcp_servers.csvtk",
+        "description": "Create new column from selected fields by regular expression."
+      },
+      {
+        "name": "csvtk_rename",
+        "module": "mcp_servers.csvtk",
+        "description": "Rename column names with new names."
+      },
+      {
+        "name": "csvtk_pretty",
+        "module": "mcp_servers.csvtk",
+        "description": "Convert CSV to a readable aligned table."
+      },
+      {
+        "name": "csvtk_csv2json",
+        "module": "mcp_servers.csvtk",
+        "description": "Convert CSV to JSON format."
+      }
+    ],
+    "data_lake": [],
+    "libraries": [
+      {
+        "name": "biopython",
+        "description": "[Python Package] A set of tools for biological computation including parsers for bioinformatics files, access to online services, and interfaces to common bioinformatics programs."
+      },
+      {
+        "name": "scikit-bio",
+        "description": "[Python Package] Data structures, algorithms, and educational resources for bioinformatics, including sequence analysis, phylogenetics, and ordination methods."
+      },
+      {
+        "name": "biotite",
+        "description": "[Python Package] A comprehensive library for computational molecular biology, providing tools for sequence analysis, structure analysis, and more."
+      },
+      {
+        "name": "gget",
+        "description": "[Python Package] A toolkit for accessing genomic databases and retrieving sequences, annotations, and other genomic data."
+      },
+      {
+        "name": "pyfaidx",
+        "description": "[Python Package] A Python package for efficient random access to FASTA files."
+      },
+      {
+        "name": "pyranges",
+        "description": "[Python Package] A Python package for interval manipulation with a pandas-like interface."
+      },
+      {
+        "name": "pybedtools",
+        "description": "[Python Package] A Python wrapper for Aaron Quinlan's BEDTools programs."
+      },
+      {
+        "name": "pandas",
+        "description": "[Python Package] A fast, powerful, and flexible data analysis and manipulation library for Python."
+      },
+      {
+        "name": "numpy",
+        "description": "[Python Package] The fundamental package for scientific computing with Python, providing support for arrays, matrices, and mathematical functions."
+      },
+      {
+        "name": "scipy",
+        "description": "[Python Package] A Python library for scientific and technical computing, including modules for optimization, linear algebra, integration, and statistics."
+      },
+      {
+        "name": "scikit-learn",
+        "description": "[Python Package] A machine learning library featuring various classification, regression, and clustering algorithms."
+      },
+      {
+        "name": "matplotlib",
+        "description": "[Python Package] A comprehensive library for creating static, animated, and interactive visualizations in Python."
+      },
+      {
+        "name": "seaborn",
+        "description": "[Python Package] A statistical data visualization library based on matplotlib with a high-level interface for drawing attractive statistical graphics."
+      },
+      {
+        "name": "tqdm",
+        "description": "[Python Package] A fast, extensible progress bar for loops and CLI applications."
+      },
+      {
+        "name": "joblib",
+        "description": "[Python Package] A set of tools to provide lightweight pipelining in Python, including transparent disk-caching and parallel computing."
+      },
+      {
+        "name": "ggplot2",
+        "description": "[R Package] A system for declaratively creating graphics, based on The Grammar of Graphics. Use with subprocess.run(['Rscript', '-e', 'library(ggplot2); ...'])."
+      },
+      {
+        "name": "dplyr",
+        "description": "[R Package] A grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges. Use with subprocess."
+      },
+      {
+        "name": "readr",
+        "description": "[R Package] A fast and friendly way to read rectangular data like CSV, TSV, and FWF. Use with subprocess.run(['Rscript', '-e', 'library(readr); ...'])."
+      },
+      {
+        "name": "stringr",
+        "description": "[R Package] A cohesive set of functions designed to make working with strings as easy as possible. Use with subprocess calls."
+      },
+      {
+        "name": "Matrix",
+        "description": "[R Package] A package that provides classes and methods for dense and sparse matrices. Required for Seurat. Use with subprocess calls."
+      }
+    ],
+    "know_how": []
+  },
+  "selected_resource_names": {
+    "tools": [
+      "analyze_protein_conservation",
+      "analyze_protein_phylogeny",
+      "analyze_comparative_genomics_and_haplotypes",
+      "interspecies_gene_conversion",
+      "annotate_bacterial_genome",
+      "run_python_repl",
+      "read_function_source_code",
+      "query_uniprot",
+      "query_interpro",
+      "query_kegg",
+      "query_stringdb",
+      "query_ensembl",
+      "blast_sequence",
+      "query_reactome",
+      "query_quickgo",
+      "track_immune_cells_under_flow",
+      "analyze_cytokine_production_in_cd4_tcells",
+      "analyze_ebv_antibody_titers",
+      "analyze_arsenic_speciation_hplc_icpms",
+      "csvtk_headers",
+      "csvtk_dim",
+      "csvtk_summary",
+      "csvtk_cut",
+      "csvtk_grep",
+      "csvtk_join",
+      "csvtk_concat",
+      "csvtk_mutate",
+      "csvtk_rename",
+      "csvtk_pretty",
+      "csvtk_csv2json"
+    ],
+    "data_lake": [],
+    "libraries": [
+      "biopython",
+      "scikit-bio",
+      "biotite",
+      "gget",
+      "pyfaidx",
+      "pyranges",
+      "pybedtools",
+      "pandas",
+      "numpy",
+      "scipy",
+      "scikit-learn",
+      "matplotlib",
+      "seaborn",
+      "tqdm",
+      "joblib",
+      "ggplot2",
+      "dplyr",
+      "readr",
+      "stringr",
+      "Matrix"
+    ],
+    "know_how": []
+  },
+  "registered_tool_count": 331,
+  "registered_tool_names": [
+    "fetch_supplementary_info_from_doi",
+    "query_arxiv",
+    "query_scholar",
+    "query_pubmed",
+    "search_google",
+    "extract_url_content",
+    "extract_pdf_content",
+    "advanced_web_search_claude",
+    "analyze_circular_dichroism_spectra",
+    "analyze_rna_secondary_structure_features",
+    "analyze_protease_kinetics",
+    "analyze_enzyme_kinetics_assay",
+    "analyze_itc_binding_thermodynamics",
+    "analyze_protein_conservation",
+    "split_modalities",
+    "prepare_input_for_nnunet",
+    "segment_with_nn_unet",
+    "create_segmentation_visualization",
+    "quick_rigid_registration",
+    "quick_affine_registration",
+    "quick_deformable_registration",
+    "batch_register_images",
+    "calculate_similarity_metrics",
+    "create_registration_visualization",
+    "analyze_cell_migration_metrics",
+    "perform_crispr_cas9_genome_editing",
+    "analyze_calcium_imaging_data",
+    "analyze_in_vitro_drug_release_kinetics",
+    "analyze_myofiber_morphology",
+    "decode_behavior_from_neural_trajectories",
+    "simulate_whole_cell_ode_model",
+    "predict_protein_disorder_regions",
+    "analyze_cell_morphology_and_cytoskeleton",
+    "analyze_tissue_deformation_flow",
+    "find_n_glycosylation_motifs",
+    "predict_o_glycosylation_hotspots",
+    "list_glycoengineering_resources",
+    "analyze_ddr_network_in_cancer",
+    "analyze_cell_senescence_and_apoptosis",
+    "detect_and_annotate_somatic_mutations",
+    "detect_and_characterize_structural_variations",
+    "perform_gene_expression_nmf_analysis",
+    "analyze_copy_number_purity_ploidy_and_focal_events",
+    "quantify_cell_cycle_phases_from_microscopy",
+    "quantify_and_cluster_cell_motility",
+    "perform_facs_cell_sorting",
+    "analyze_flow_cytometry_immunophenotyping",
+    "analyze_mitochondrial_morphology_and_potential",
+    "annotate_open_reading_frames",
+    "annotate_plasmid",
+    "get_gene_coding_sequence",
+    "get_plasmid_sequence",
+    "align_sequences",
+    "pcr_simple",
+    "digest_sequence",
+    "find_restriction_sites",
+    "find_restriction_enzymes",
+    "find_sequence_mutations",
+    "design_knockout_sgrna",
+    "get_oligo_annealing_protocol",
+    "get_golden_gate_assembly_protocol",
+    "get_bacterial_transformation_protocol",
+    "design_primer",
+    "design_verification_primers",
+    "design_golden_gate_oligos",
+    "golden_gate_assembly",
+    "liftover_coordinates",
+    "bayesian_finemapping_with_deep_vi",
+    "analyze_cas9_mutation_outcomes",
+    "analyze_crispr_genome_editing",
+    "simulate_demographic_history",
+    "identify_transcription_factor_binding_sites",
+    "fit_genomic_prediction_model",
+    "perform_pcr_and_gel_electrophoresis",
+    "analyze_protein_phylogeny",
+    "annotate_celltype_scRNA",
+    "annotate_celltype_with_panhumanpy",
+    "create_scvi_embeddings_scRNA",
+    "create_harmony_embeddings_scRNA",
+    "get_uce_embeddings_scRNA",
+    "map_to_ima_interpret_scRNA",
+    "get_rna_seq_archs4",
+    "get_gene_set_enrichment_analysis_supported_database_list",
+    "gene_set_enrichment_analysis",
+    "analyze_chromatin_interactions",
+    "analyze_comparative_genomics_and_haplotypes",
+    "perform_chipseq_peak_calling_with_macs2",
+    "find_enriched_motifs_with_homer",
+    "analyze_genomic_region_overlap",
+    "unsupervised_celltype_transfer_between_scRNA_datasets",
+    "generate_embeddings_with_state",
+    "interspecies_gene_conversion",
+    "generate_gene_embeddings_with_ESM_models",
+    "generate_transcriptformer_embeddings",
+    "analyze_atac_seq_differential_accessibility",
+    "analyze_bacterial_growth_curve",
+    "isolate_purify_immune_cells",
+    "estimate_cell_cycle_phase_durations",
+    "track_immune_cells_under_flow",
+    "analyze_cfse_cell_proliferation",
+    "analyze_cytokine_production_in_cd4_tcells",
+    "analyze_ebv_antibody_titers",
+    "analyze_cns_lesion_histology",
+    "analyze_immunohistochemistry_image",
+    "optimize_anaerobic_digestion_process",
+    "analyze_arsenic_speciation_hplc_icpms",
+    "count_bacterial_colonies",
+    "annotate_bacterial_genome",
+    "enumerate_bacterial_cfu_by_serial_dilution",
+    "model_bacterial_growth_dynamics",
+    "quantify_biofilm_biomass_crystal_violet",
+    "segment_and_analyze_microbial_cells",
+    "segment_cells_with_deep_learning",
+    "simulate_generalized_lotka_volterra_dynamics",
+    "predict_rna_secondary_structure",
+    "simulate_microbial_population_dynamics",
+    "analyze_aortic_diameter_and_geometry",
+    "analyze_atp_luminescence_assay",
+    "analyze_thrombus_histology",
+    "analyze_intracellular_calcium_with_rhod2",
+    "quantify_corneal_nerve_fibers",
+    "segment_and_quantify_cells_in_multiplexed_images",
+    "analyze_bone_microct_morphometry",
+    "run_diffdock_with_smiles",
+    "docking_autodock_vina",
+    "run_autosite",
+    "retrieve_topk_repurposing_drugs_from_disease_txgnn",
+    "predict_admet_properties",
+    "predict_binding_affinity_protein_1d_sequence",
+    "analyze_accelerated_stability_of_pharmaceutical_formulations",
+    "run_3d_chondrogenic_aggregate_assay",
+    "grade_adverse_events_using_vcog_ctcae",
+    "analyze_radiolabeled_antibody_biodistribution",
+    "estimate_alpha_particle_radiotherapy_dosimetry",
+    "perform_mwas_cyp2c19_metabolizer_status",
+    "calculate_physicochemical_properties",
+    "analyze_xenograft_tumor_growth_inhibition",
+    "analyze_pixel_distribution",
+    "find_roi_from_image",
+    "analyze_western_blot",
+    "query_drug_interactions",
+    "check_drug_combination_safety",
+    "analyze_interaction_mechanisms",
+    "find_alternative_drugs_ddinter",
+    "query_fda_adverse_events",
+    "get_fda_drug_label_info",
+    "check_fda_drug_recalls",
+    "analyze_fda_safety_signals",
+    "reconstruct_3d_face_from_mri",
+    "analyze_abr_waveform_p1_metrics",
+    "analyze_ciliary_beat_frequency",
+    "analyze_protein_colocalization",
+    "perform_cosinor_analysis",
+    "calculate_brain_adc_map",
+    "analyze_endolysosomal_calcium_dynamics",
+    "analyze_fatty_acid_composition_by_gc",
+    "analyze_hemodynamic_data",
+    "simulate_thyroid_hormone_pharmacokinetics",
+    "quantify_amyloid_beta_plaques",
+    "engineer_bacterial_genome_for_therapeutic_delivery",
+    "analyze_bacterial_growth_rate",
+    "analyze_barcode_sequencing_data",
+    "analyze_bifurcation_diagram",
+    "create_biochemical_network_sbml_model",
+    "optimize_codons_for_heterologous_expression",
+    "simulate_gene_circuit_with_growth_feedback",
+    "identify_fas_functional_domains",
+    "perform_flux_balance_analysis",
+    "model_protein_dimerization_network",
+    "simulate_metabolic_network_perturbation",
+    "simulate_protein_signaling_network",
+    "compare_protein_structures",
+    "simulate_renin_angiotensin_system_dynamics",
+    "query_chatnt",
+    "run_python_repl",
+    "read_function_source_code",
+    "download_synapse_data",
+    "query_uniprot",
+    "query_alphafold",
+    "query_interpro",
+    "query_pdb",
+    "query_pdb_identifiers",
+    "query_kegg",
+    "query_stringdb",
+    "query_iucn",
+    "query_paleobiology",
+    "query_jaspar",
+    "query_worms",
+    "query_cbioportal",
+    "query_clinvar",
+    "query_geo",
+    "query_dbsnp",
+    "query_ucsc",
+    "query_ensembl",
+    "query_opentarget",
+    "query_monarch",
+    "query_openfda",
+    "query_gwas_catalog",
+    "query_gnomad",
+    "blast_sequence",
+    "query_reactome",
+    "query_regulomedb",
+    "query_pride",
+    "query_gtopdb",
+    "query_remap",
+    "query_mpd",
+    "query_emdb",
+    "query_synapse",
+    "query_pubchem",
+    "query_chembl",
+    "query_unichem",
+    "query_clinicaltrials",
+    "query_dailymed",
+    "query_quickgo",
+    "query_encode",
+    "region_to_ccre_screen",
+    "get_genes_near_ccre",
+    "test_pylabrobot_script",
+    "get_pylabrobot_documentation_liquid",
+    "get_pylabrobot_documentation_material",
+    "search_protocols",
+    "get_protocol_details",
+    "list_local_protocols",
+    "read_local_protocol",
+    "kallisto_index",
+    "kallisto_quant",
+    "kallisto_bus",
+    "kallisto_quant_tcc",
+    "kallisto_h5dump",
+    "kallisto_inspect",
+    "kallisto_version",
+    "kallisto_cite",
+    "kallisto_bus_list_technologies",
+    "kallisto_merge",
+    "kraken2_classify",
+    "kraken2_build_db",
+    "kraken2_inspect_db",
+    "csvtk_headers",
+    "csvtk_dim",
+    "csvtk_ncol",
+    "csvtk_nrow",
+    "csvtk_corr",
+    "csvtk_summary",
+    "csvtk_cut",
+    "csvtk_grep",
+    "csvtk_filter",
+    "csvtk_filter2",
+    "csvtk_sort",
+    "csvtk_join",
+    "csvtk_concat",
+    "csvtk_uniq",
+    "csvtk_freq",
+    "csvtk_mutate",
+    "csvtk_mutate2",
+    "csvtk_rename",
+    "csvtk_replace",
+    "csvtk_round",
+    "csvtk_transpose",
+    "csvtk_sep",
+    "csvtk_gather",
+    "csvtk_spread",
+    "csvtk_pretty",
+    "csvtk_csv2md",
+    "csvtk_csv2json",
+    "csvtk_xlsx2csv",
+    "csvtk_fix",
+    "csvtk_fix_quotes",
+    "csvtk_del_quotes",
+    "csvtk_head",
+    "csvtk_sample",
+    "csvtk_split",
+    "csvtk_comb",
+    "csvtk_fmtdate",
+    "csvtk_fold",
+    "csvtk_unfold",
+    "csvtk_plot",
+    "csvtk_version",
+    "megahit_assemble",
+    "megahit_core_contig2fastg",
+    "kaiju_classify",
+    "kaiju_makedb",
+    "kaiju_mkbwt",
+    "kaiju_mkfmi",
+    "kaiju_multi_classify",
+    "kaiju2krona",
+    "kaiju2table",
+    "kaiju_add_taxon_names",
+    "kaiju_merge_outputs",
+    "kaijux_search",
+    "kaijup_search",
+    "fastp_tool",
+    "spades_py",
+    "metaspades_py",
+    "rnaspades_py",
+    "plasmidspades_py",
+    "metaviralspades_py",
+    "coronaspades_py",
+    "biosyntheticspades_py",
+    "spades_test",
+    "spades_kmercount",
+    "spades_hammer",
+    "settings",
+    "scanpy_filter",
+    "scanpy_norm",
+    "scanpy_log1p",
+    "scanpy_hvg",
+    "scanpy_scale",
+    "scanpy_pca",
+    "scanpy_neighbors",
+    "scanpy_umap",
+    "scanpy_tsne",
+    "scanpy_diffexp",
+    "scanpy_louvain",
+    "scanpy_leiden",
+    "scanpy_paga",
+    "scanpy_cli_read",
+    "scanpy_cli_filter",
+    "scanpy_cli_norm",
+    "scanpy_cli_hvg",
+    "scanpy_cli_scale",
+    "scanpy_cli_regress",
+    "scanpy_cli_pca",
+    "scanpy_cli_neighbor",
+    "scanpy_cli_embed",
+    "scanpy_cli_cluster",
+    "scanpy_cli_diffexp",
+    "scanpy_cli_paga",
+    "scanpy_cli_dpt",
+    "scanpy_cli_integrate",
+    "scanpy_cli_multiplet",
+    "scanpy_cli_plot"
+  ]
+}

Biomni/experiments/bioagent_bench/runs/scale_100/comparative-genomics_20260514_172511/run_summary.json

@@ -0,0 +1,17 @@
+{
+  "task_id": "comparative-genomics",
+  "run_dir": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511",
+  "outputs": [
+    {
+      "path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/cluster_annotation_mapping.csv",
+      "exists": true,
+      "size_bytes": 52250
+    }
+  ],
+  "planning_latency_seconds": 25.24140142649412,
+  "total_runtime_seconds": 6987.9035994187,
+  "final_answer_path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/final_answer.txt",
+  "metadata_path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/run_metadata.json",
+  "retrieval_plan_path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/retrieval_plan.json",
+  "output_validation_path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/comparative-genomics_20260514_172511/output_validation.json"
+}

Biomni/experiments/bioagent_bench/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv

@@ -0,0 +1,2 @@
+chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id
+7,117227832,7115,G,T,CFTR,ENSG00000001626,stop_gained,HIGH,ENST00000003084,c.1624G>T,p.Gly542*,Pathogenic,Cystic_fibrosis; Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation; Hereditary_pancreatitis; Bronchiectasis_with_or_without_elevated_sweat_chloride_1; CFTR-related_disorder; not_provided,practice_guideline,rs113993959

Biomni/experiments/bioagent_bench/runs/scale_100/cystic-fibrosis_20260514_192139/execution_log.json

@@ -0,0 +1,34 @@
+{
+  "log_entries": [
+    "================================ Human Message =================================\n\nYou are running a bioagent-bench task with local files already prepared.\n\nTask ID: cystic-fibrosis\nTask name: Cystic Fibrosis Mendelian Variant Identification\nBenchmark prompt:\nFind the genetic cause of Cystic fibrosis; identify the causal recessive variant consistent with affected siblings NA12885, NA12886, and NA12879. The output should be a CSV file with the following columns: chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id. <example>chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id\nX,123456789,VAR123,A,G,GENE1,ENSG00000000001,missense_variant,MODERATE,ENST00000000001,c.123A>G,p.Lys41Arg,Likely_pathogenic,Disease_A; Disease_B; not_provided,reviewed_by_expert_panel,rs0000001</example>\nData background:\nThe sample dataset is a simulated dataset for finding the genetic cause of Cystic fibrosis. The dataset is real sequencing data from CEPH_1463 dataset provided by the Complete Genomics Diversity Panel. It consists of sequencing of a family: 4 grandparents, 2 parents and 11 siblings. A known Mandelian disease mutation has been added on three siblings, taking care to be consistent with the underlying heplotype structure. The goal is to find the mutation causing the mendalian recessive trait - Cystic Fibrosis.\n\nConstraints:\n1. Use only the benchmark inputs and references explicitly listed below.\n2. Save the required final deliverables exactly to the paths listed below.\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\n5. Return a concise final summary after writing the required files.\n\nTask-specific instruction:\nUse only the provided family variant data and ClinVar VCF. The final row should identify the causal CFTR recessive variant consistent with affected siblings.\n\nBenchmark data policy:\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/data\n- Allowed reference directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/results\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than cystic-fibrosis>\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\n- Do not download external databases or install new packages during the benchmark run.\n\nInput data directory:\n/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data\nVisible input files:\n- ex1.eff.vcf\n- family_description.txt\n\nReference data directory:\n/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference\nVisible reference files:\n- clinvar_20250521.vcf.gz\n- clinvar_20250521.vcf.gz.tbi\n\nRequired final output paths:\n- cf_variants.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv",
+    "================================== Ai Message ==================================\n\nI'll start by examining the input data files and understanding the family structure, then identify the causal CFTR variant.\n\n## Plan\n\n1. [ ] Read the family description to understand the pedigree and affected individuals\n2. [ ] Examine the family VCF (ex1.eff.vcf) to understand its structure\n3. [ ] Filter for variants in affected siblings (NA12885, NA12886, NA12879) that follow recessive inheritance pattern\n4. [ ] Cross-reference with ClinVar VCF to find CFTR variants with clinical significance\n5. [ ] Identify the causal recessive variant consistent with affected siblings\n6. [ ] Format and save the output CSV\n\nLet me start with step 1.\n\n<execute>\n# Step 1: Read the family description\nwith open(\"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/family_description.txt\", \"r\") as f:\n    print(f.read())\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>This is a three-generation family with the following structure:\n\nFirst Generation (Grandparents):\n- Paternal grandfather: NA12889 (unaffected male)\n- Paternal grandmother: NA12890 (unaffected female)\n- Maternal grandfather: NA12891 (unaffected male)\n- Maternal grandmother: NA12892 (unaffected female)\n\nSecond Generation (Parents):\n- Father: NA12877 (unaffected male, child of NA12889 and NA12890)\n- Mother: NA12878 (unaffected female, child of NA12891 and NA12892)\n\nThird Generation (Children of NA12877 and NA12878):\n1. NA12879 (affected female)\n2. NA12880 (unaffected female)\n3. NA12881 (unaffected female)\n4. NA12882 (unaffected male)\n5. NA12883 (unaffected male)\n6. NA12884 (unaffected male)\n7. NA12885 (affected female)\n8. NA12886 (affected male)\n9. NA12887 (unaffected female)\n10. NA12888 (unaffected male)\n11. NA12893 (unaffected male)\n</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\n# Step 2: Examine the VCF file structure\nimport subprocess\n# Check the header and first few lines\nresult = subprocess.run([\"head\", \"-n\", \"50\", \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"], capture_output=True, text=True)\nprint(result.stdout)\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>##fileformat=VCFv4.1\n##fileDate=20120730\n##center=Complete Genomics\n##source=CGAPipeline_2.0.0.26;cgatools_1.6.0\n##source_GENOME_REFERENCE=NCBI build 37\n##phasing=partial\n##ALT=<ID=CGA_NOCALL,Description=\"No-called record\">\n##ALT=<ID=CGA_CNVWIN,Description=\"Copy number analysis window\">\n##ALT=<ID=INS:ME:ALU,Description=\"Insertion of ALU element\">\n##ALT=<ID=INS:ME:L1,Description=\"Insertion of L1 element\">\n##ALT=<ID=INS:ME:SVA,Description=\"Insertion of SVA element\">\n##ALT=<ID=INS:ME:MER,Description=\"Insertion of MER element\">\n##ALT=<ID=INS:ME:LTR,Description=\"Insertion of LTR element\">\n##ALT=<ID=INS:ME:PolyA,Description=\"Insertion of PolyA element\">\n##ALT=<ID=INS:ME:HERV,Description=\"Insertion of HERV element\">\n##FILTER=<ID=VQLOW,Description=\"Quality not VQHIGH\">\n##FILTER=<ID=SQLOW,Description=\"Somatic quality not SQHIGH\">\n##FILTER=<ID=URR,Description=\"Too close to an underrepresented repeat\">\n##FILTER=<ID=MPCBT,Description=\"Mate pair count below 10\">\n##FILTER=<ID=SHORT,Description=\"Junction side length below 70\">\n##FILTER=<ID=TSNR,Description=\"Transition sequence not resolved\">\n##FILTER=<ID=INTERBL,Description=\"Interchromosomal junction in baseline\">\n##FILTER=<ID=sns75,Description=\"Sensitivity to known MEI calls in range (.75,.95] i.e. medium FDR\">\n##FILTER=<ID=sns95,Description=\"Sensitivity to known MEI calls in range (.95,1.00] i.e. high to very high FDR\">\n##INFO=<ID=END,Number=1,Type=Integer,Description=\"End position of the variant described in this record\">\n##INFO=<ID=SVTYPE,Number=1,Type=String,Description=\"Type of structural variant\">\n##INFO=<ID=IMPRECISE,Number=0,Type=Flag,Description=\"Imprecise structural variation\">\n##INFO=<ID=SVLEN,Number=.,Type=Integer,Description=\"Difference in length between REF and ALT alleles\">\n##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">\n##source_20130711.1=vcf-merge(r840) --collapse any --remove-duplicates --trim-ALTs --regions 1 /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12877.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12878.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12879.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12880.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12881.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12882.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12883.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12884.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12885.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12886.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12887.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12888.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12889.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12890.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12891.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12892.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12893.vcf.gz\n##sourceFiles_20130711.1=0:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12877.vcf.gz,1:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12878.vcf.gz,2:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12879.vcf.gz,3:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12880.vcf.gz,4:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12881.vcf.gz,5:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12882.vcf.gz,6:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12883.vcf.gz,7:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12884.vcf.gz,8:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12885.vcf.gz,9:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12886.vcf.gz,10:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12887.vcf.gz,11:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12888.vcf.gz,12:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12889.vcf.gz,13:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12890.vcf.gz,14:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12891.vcf.gz,15:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12892.vcf.gz,16:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12893.vcf.gz\n##INFO=<ID=SF,Number=.,Type=String,Description=\"Source File (index to sourceFiles, f when filtered)\">\n##INFO=<ID=AC,Number=.,Type=Integer,Description=\"Allele count in genotypes\">\n##INFO=<ID=AN,Number=1,Type=Integer,Description=\"Total number of alleles in called genotypes\">\n##SnpEffVersion=\"5.2 (build 2023-09-29 06:17), by Pablo Cingolani\"\n##SnpEffCmd=\"SnpEff  -lof GRCh37.75 /home/dev/bioagent-bench/tasks/fibrosis/data/protocols/ex1.vcf \"\n##INFO=<ID=ANN,Number=.,Type=String,Description=\"Functional annotations: 'Allele | Annotation | Annotation_Impact | Gene_Name | Gene_ID | Feature_Type | Feature_ID | Transcript_BioType | Rank | HGVS.c | HGVS.p | cDNA.pos / cDNA.length | CDS.pos / CDS.length | AA.pos / AA.length | Distance | ERRORS / WARNINGS / INFO' \">\n##INFO=<ID=LOF,Number=.,Type=String,Description=\"Predicted loss of function effects for this variant. Format: 'Gene_Name | Gene_ID | Number_of_transcripts_in_gene | Percent_of_transcripts_affected'\">\n##INFO=<ID=NMD,Number=.,Type=String,Description=\"Predicted nonsense mediated decay effects for this variant. Format: 'Gene_Name | Gene_ID | Number_of_transcripts_in_gene | Percent_of_transcripts_affected'\">\n#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tNA12877\tNA12878\tNA12879\tNA12880\tNA12881\tNA12882\tNA12883\tNA12884\tNA12885\tNA12886\tNA12887\tNA12888\tNA12889\tNA12890\tNA12891\tNA12892\tNA12893\n7\t16487\t.\tG\tT\t.\t.\tAC=9;AN=9;ANN=T|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16487G>T||||||\tGT\t1|1\t.\t.\t.\t.\t1|1\t.\t.\t.\t.\t.\t1|.\t.\t1|1\t1|1\t.\t.\n7\t16671\t.\tT\tC\t.\t.\tAC=2;AN=2;ANN=C|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16671T>C||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t1|.\t1|.\t.\t.\t.\t.\n7\t16692\t.\tG\tC\t.\t.\tAC=6;AN=6;ANN=C|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16692G>C||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t1|.\t1|1\t.\t1|.\t1/1\t.\n7\t16712\t.\tT\tG\t.\t.\tAC=1;AN=2;ANN=G|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16712T>G||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t0|1\t.\t.\t.\t.\n7\t16717\t.\tCTG\tATA\t.\t.\tAC=1;AN=1;ANN=ATA|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16717_16719delCTGinsATA||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.|1\t.\t.\t.\t.\n7\t16719\t.\tG\tA\t.\t.\tAC=2;AN=2;ANN=A|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16719G>A||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t1/1\t.\n7\t16787\t.\tAAATCCG\tA\t.\t.\tAC=1;AN=2;ANN=A|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16788_16793delAATCCG||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t1|0\t.\n7\t16798\t.\tT\tC\t.\t.\tAC=1;AN=2;ANN=C|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16798T>C||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t1|0\t.\n7\t16878\t.\tA\tT\t.\t.\tAC=1;AN=1;ANN=T|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16878A>T||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.|1\t.\n7\t17881\t.\tT\tTCAA\t.\t.\tAC=2;AN=2;ANN=TCAA|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.17881_17882insCAA||||||\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t1/1\t.\t.\t.\n\n</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\n# Step 2 continued: Understand the VCF structure - check column headers and more data\nimport subprocess\n\n# Check the column names (samples) from the VCF header\nresult = subprocess.run([\"grep\", \"^#CHROM\", \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"], capture_output=True, text=True)\nprint(\"Column headers:\")\nprint(result.stdout)\n\n# Let me also count total lines\nresult2 = subprocess.run([\"wc\", \"-l\", \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"], capture_output=True, text=True)\nprint(f\"\\nTotal lines: {result2.stdout}\")\n\n# Check what chromosomes are present\nresult3 = subprocess.run([\"grep\", \"-v\", \"^#\", \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"], capture_output=True, text=True)\nimport pandas as pd\nimport io\n# Parse just the first columns to check chromosomes\nlines = result3.stdout.strip().split('\\n')\nchromes = set()\nfor line in lines[:1000]:\n    if line:\n        chromes.add(line.split('\\t')[0])\nprint(f\"\\nChromosomes found (first 1000 lines): {sorted(chromes)}\")\nprint(f\"Total variant lines: {len(lines)}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Column headers:\n#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tNA12877\tNA12878\tNA12879\tNA12880\tNA12881\tNA12882\tNA12883\tNA12884\tNA12885\tNA12886\tNA12887\tNA12888\tNA12889\tNA12890\tNA12891\tNA12892\tNA12893\n\n\nTotal lines: 743194 /225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\n\n\nChromosomes found (first 1000 lines): ['7']\nTotal variant lines: 743154\n</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\n# Step 3: Find CFTR variants in the VCF with recessive inheritance pattern\n# CFTR gene location on GRCh37: chr7:117,120,016-117,308,718\n\nimport subprocess\nimport pandas as pd\nimport io\n\n# Use cyvcf2 for efficient VCF parsing\nfrom cyvcf2 import VCF\n\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# Affected siblings\naffected = ['NA12879', 'NA12885', 'NA12886']\n# Parents (should be carriers - heterozygous)\nparents = ['NA12877', 'NA12878']\n# All samples list from the VCF header\nall_samples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', \n               'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', \n               'NA12891', 'NA12892', 'NA12893']\n# Unaffected siblings (not affected, not parents)\nunaffected = ['NA12880', 'NA12881', 'NA12882', 'NA12883', 'NA12884', 'NA12887', 'NA12888', 'NA12893']\n# Grandparents\ngrandparents = ['NA12889', 'NA12890', 'NA12891', 'NA12892']\n\n# Open VCF\nvcf = VCF(vcf_path)\n\n# Focus on CFTR region\ncftr_chrom = '7'\ncftr_start = 117120016\ncftr_end = 117308718\n\nprint(\"Searching for variants in CFTR gene region...\")\nprint(f\"CFTR region: chr{cftr_chrom}:{cftr_start}-{cftr_end}\")\n\ncandidate_variants = []\n\nfor variant in vcf(f'{cftr_chrom}:{cftr_start}-{cftr_end}'):\n    # Get genotypes for all samples\n    genotypes = variant.genotypes\n    \n    # For recessive: affected should be homozygous alt (1/1)\n    affected_hom_alt = all(\n        genotypes[all_samples.index(s)][0] == 1 and \n        genotypes[all_samples.index(s)][1] == 1 and\n        genotypes[all_samples.index(s)][0] == genotypes[all_samples.index(s)][1]\n        for s in affected\n    )\n    \n    if not affected_hom_alt:\n        continue\n    \n    # Parents should be heterozygous (0/1)\n    parents_het = all(\n        genotypes[all_samples.index(s)][0] == 0 and \n        genotypes[all_samples.index(s)][1] == 1\n        for s in parents\n    )\n    \n    if not parents_het:\n        continue\n    \n    # Check if unaffected siblings are not homozygous alt\n    unaffected_not_hom_alt = all(\n        not (genotypes[all_samples.index(s)][0] == 1 and \n             genotypes[all_samples.index(s)][1] == 1)\n        for s in unaffected\n    )\n    \n    if not unaffected_not_hom_alt:\n        continue\n    \n    # Get annotation info\n    ann = variant.INFO.get('ANN', '')\n    \n    candidate = {\n        'chromosome': variant.CHROM,\n        'position': variant.POS,\n        'variant_id': variant.ID if variant.ID else '.',\n        'reference': variant.REF,\n        'alternate': ','.join(variant.ALT),\n        'ANN': ann,\n        'qual': variant.QUAL,\n        'filter': variant.FILTER,\n    }\n    \n    # Also print genotypes for all relevant samples\n    print(f\"\\nFound candidate at chr{variant.CHROM}:{variant.POS} {variant.REF}>{','.join(variant.ALT)}\")\n    for s in all_samples:\n        idx = all_samples.index(s)\n        gt = f\"{genotypes[idx][0]}|{genotypes[idx][1]}\"\n        marker = \"\"\n        if s in affected:\n            marker = \" [AFFECTED]\"\n        elif s in parents:\n            marker = \" [PARENT]\"\n        elif s in unaffected:\n            marker = \" [UNAFFECTED]\"\n        print(f\"  {s}: {gt}{marker}\")\n    \n    candidate_variants.append(candidate)\n\nvcf.close()\n\nprint(f\"\\n\\nTotal candidates with recessive pattern: {len(candidate_variants)}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Error: No module named 'cyvcf2'</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\n# Step 3 alternative: Use command-line tools to parse the VCF file\nimport subprocess\nimport pandas as pd\nimport io\n\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# First, let's check what's on chromosome 7 around the CFTR gene region\n# CFTR on GRCh37: chr7:117,120,016-117,308,718\n\n# Let's use awk to extract variants in CFTR region from the VCF\nresult = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} $1==\"7\" && $2>=117120016 && $2<=117308718 {print}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines = result.stdout.strip().split('\\n')\nprint(f\"Number of variants in CFTR region: {len(lines)}\")\nif len(lines) > 0:\n    for line in lines[:5]:\n        print(line)\nelse:\n    print(\"No variants found in that range. Let me check what variants are on chromosome 7...\")\n    \n# Let me check general chromosome 7 variants\nresult2 = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    '$1==\"7\" {print $1, $2, $4, $5}',\n    vcf_path\n], capture_output=True, text=True)\nlines2 = result2.stdout.strip().split('\\n')\nprint(f\"\\nTotal chr7 variants: {len(lines2)}\")\nprint(\"First few:\")\nfor line in lines2[:5]:\n    print(line)\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Number of variants in CFTR region: 354\n7\t117122535\t.\tA\tG\t.\t.\tAC=1;AN=2;ANN=G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+2334A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+6673A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+2787A>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+2334A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+2334A>G||||||WARNING_TRANSCRIPT_INCOMPLETE\tGT\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t1|0\t.\t.\n7\t117124163\t.\tA\tC\t.\t.\tAC=1;AN=2;ANN=C|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-3628A>C|||||3628|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+3962A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+8301A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+4415A>C||||||WARNING_TRANSCRIPT_INCOMPLETE,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+3962A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+3962A>C||||||WARNING_TRANSCRIPT_INCOMPLETE\tGT\t.\t.\t.\t.\t1/0\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\n7\t117124798\t.\tT\tG\t.\t.\tAC=1;AN=2;ANN=G|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-2993T>G|||||2993|,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+4597T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+8936T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+5050T>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+4597T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+4597T>G||||||WARNING_TRANSCRIPT_INCOMPLETE\tGT\t1|0\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\n7\t117125573\t.\tA\tG\t.\t.\tAC=1;AN=2;ANN=G|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-2218A>G|||||2218|,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+5372A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+9711A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+5825A>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+5372A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+5372A>G||||||WARNING_TRANSCRIPT_INCOMPLETE\tGT\t.\t.\t.\t.\t1/0\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\t.\n7\t117126401\t.\tG\tA\t.\t.\tAC=8;AN=16;ANN=A|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-1390G>A|||||1390|,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+6200G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+10539G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+6653G>A||||||WARNING_TRANSCRIPT_INCOMPLETE,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+6200G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+6200G>A||||||WARNING_TRANSCRIPT_INCOMPLETE\tGT\t1/0\t.\t.\t1/0\t1/0\t1/0\t.\t.\t.\t.\t1/0\t1/0\t.\t1/0\t.\t.\t1/0\n\nTotal chr7 variants: 499080\nFirst few:\n7 16487 G T\n7 16671 T C\n7 16692 G C\n7 16712 T G\n7 16717 CTG ATA\n</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\n# Now let's parse the VCF for CFTR region more carefully using Python\nimport subprocess\nimport re\n\n# Extract the CFTR region variants with their genotypes\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# Headers\nresult = subprocess.run([\"grep\", \"^#CHROM\", vcf_path], capture_output=True, text=True)\nheaders = result.stdout.strip().split('\\t')\nprint(\"Samples order:\", headers[9:])\n\n# Extract all CFTR region variants\nresult = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} $1==\"7\" && $2>=117120016 && $2<=117308718 {print}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines = result.stdout.strip().split('\\n')\nprint(f\"Total CFTR region variants: {len(lines)}\")\n\n# Parse each line\nimport pandas as pd\n\nrecords = []\nfor line in lines:\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    chrom = fields[0]\n    pos = fields[1]\n    vid = fields[2]\n    ref = fields[3]\n    alt = fields[4]\n    qual = fields[5]\n    filt = fields[6]\n    info = fields[7]\n    fmt = fields[8]\n    gts = fields[9:]\n    \n    # Parse ANN field\n    ann_match = re.search(r'ANN=([^\\t]+)', info)\n    ann = ann_match.group(1) if ann_match else ''\n    \n    record = {\n        'chromosome': chrom,\n        'position': int(pos),\n        'variant_id': vid,\n        'reference': ref,\n        'alternate': alt,\n        'ANN': ann,\n    }\n    \n    # Parse genotypes for each sample\n    samples = headers[9:]\n    for i, s in enumerate(samples):\n        record[s] = gts[i]\n    \n    records.append(record)\n\ndf = pd.DataFrame(records)\nprint(f\"\\nDataFrame shape: {df.shape}\")\nprint(\"\\nFirst few rows:\")\nprint(df[['chromosome', 'position', 'reference', 'alternate', 'ANN']].head(10).to_string())\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>The output is too long to be added to context. Here are the first 10K characters...\nSamples order: ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', 'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', 'NA12891', 'NA12892', 'NA12893']\nTotal CFTR region variants: 354\n\nDataFrame shape: (354, 23)\n\nFirst few rows:\n  chromosome   position reference alternate                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            ANN\n0          7  117122535         A         G                                                                                                                                                                                                                                                                                                                                          G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+2334A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+6673A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+2787A>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+2334A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+2334A>G||||||WARNING_TRANSCRIPT_INCOMPLETE\n1          7  117124163         A         C                                                                                                                                                                                                        C|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-3628A>C|||||3628|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+3962A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+8301A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+4415A>C||||||WARNING_TRANSCRIPT_INCOMPLETE,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+3962A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+3962A>C||||||WARNING_TRANSCRIPT_INCOMPLETE\n2          7  117124798         T         G                                                                                                                                                                                                        G|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-2993T>G|||||2993|,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+4597T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+8936T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+5050T>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+4597T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+4597T>G||||||WARNING_TRANSCRIPT_INCOMPLETE\n3          7  117125573         A         G                                                                                                                                                                                                        G|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-2218A>G|||||2218|,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+5372A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+9711A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+5825A>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+5372A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+5372A>G||||||WARNING_TRANSCRIPT_INCOMPLETE\n4          7  117126401         G         A                                                                                                                                                                                                       A|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-1390G>A|||||1390|,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+6200G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+10539G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+6653G>A||||||WARNING_TRANSCRIPT_INCOMPLETE,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+6200G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+6200G>A||||||WARNING_TRANSCRIPT_INCOMPLETE\n5          7  117126950         G     GGAGA  GGAGA|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-841_-840insGAGA|||||840|,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+6768_53+6771dupGAGA||||||INFO_REALIGN_3_PRIME,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+11107_166+11110dupGAGA||||||INFO_REALIGN_3_PRIME,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+7221_-191+7224dupGAGA||||||WARNING_TRANSCRIPT_INCOMPLETE&INFO_REALIGN_3_PRIME,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+6768_53+6771dupGAGA||||||INFO_REALIGN_3_PRIME,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+6768_53+6771dupGAGA||||||WARNING_TRANSCRIPT_INCOMPLETE&INFO_REALIGN_3_PRIME\n6          7  117127739         A         C                                                                                                                                                                                                           C|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-52A>C|||||52|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+7538A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+11877A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+7991A>C||||||WARNING_TRANSCRIPT_INCOMPLETE,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+7538A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+7538A>C||||||WARNING_TRANSCRIPT_INCOMPLETE\n7          7  117128942         A         C                                                                                                                                                                                                       C|downstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.*959A>C|||||959|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+8741A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+13080A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+9194A>C||||||WARNING_TRANSCRIPT_INCOMPLETE,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+8741A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+8741A>C||||||WARNING_TRANSCRIPT_INCOMPLETE\n8          7  117129404         T         C                                                                                                                                                                                                     C|downstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.*1421T>C|||||1421|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+9203T>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+13542T>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\nimport subprocess\nimport re\nimport pandas as pd\n\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# Extract CFTR region variants with genotypes\nresult = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} $1==\"7\" && $2>=117120016 && $2<=117308718 {print}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines = result.stdout.strip().split('\\n')\nprint(f\"Total CFTR region variants: {len(lines)}\")\n\n# Parse genotypes specifically\naffected = ['NA12879', 'NA12885', 'NA12886']\nparents = ['NA12877', 'NA12878']\nunaffected_siblings = ['NA12880', 'NA12881', 'NA12882', 'NA12883', 'NA12884', 'NA12887', 'NA12888', 'NA12893']\ngrandparents = ['NA12889', 'NA12890', 'NA12891', 'NA12892']\n\n# Samples order from VCF\nsamples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', \n           'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', \n           'NA12891', 'NA12892', 'NA12893']\n\ndef parse_gt(gt_field):\n    \"\"\"Parse genotype field, handling . and | formats\"\"\"\n    gt_field = gt_field.strip()\n    if gt_field == '.' or gt_field == '':\n        return None\n    \n    # Handle phased (0|1) and unphased (0/1) formats\n    for sep in ['|', '/']:\n        if sep in gt_field:\n            parts = gt_field.split(sep)\n            try:\n                a1 = int(parts[0]) if parts[0] != '.' else None\n                a2 = int(parts[1]) if parts[1] != '.' else None\n                return (a1, a2)\n            except (ValueError, IndexError):\n                return None\n    \n    # Handle the case where it's just a number with phasing\n    try:\n        return (int(gt_field), int(gt_field))\n    except ValueError:\n        return None\n\ncandidates = []\nfor line in lines:\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    chrom = fields[0]\n    pos = fields[1]\n    vid = fields[2]\n    ref = fields[3]\n    alt = fields[4]\n    info = fields[7]\n    gts = fields[9:]\n    \n    # Parse genotypes\n    sample_gts = {}\n    for i, s in enumerate(samples):\n        sample_gts[s] = parse_gt(gts[i])\n    \n    # Check if all affected are homozygous alt (1/1)\n    affected_ok = True\n    for s in affected:\n        gt = sample_gts[s]\n        if gt is None or gt[0] != 1 or gt[1] != 1:\n            affected_ok = False\n            break\n    \n    if not affected_ok:\n        continue\n    \n    # Check parents are heterozygous (0/1)\n    parents_ok = True\n    for s in parents:\n        gt = sample_gts[s]\n        if gt is None or (gt[0], gt[1]) != (0, 1):\n            parents_ok = False\n            break\n    \n    if not parents_ok:\n        continue\n    \n    # Check unaffected siblings are NOT homozygous alt\n    unaffected_ok = True\n    for s in unaffected_siblings:\n        gt = sample_gts[s]\n        if gt is not None and gt[0] == 1 and gt[1] == 1:\n            unaffected_ok = False\n            break\n    \n    if not unaffected_ok:\n        continue\n    \n    # This is a candidate\n    # Extract ANN info\n    ann_match = re.search(r'ANN=([^\\t]+)', info)\n    ann = ann_match.group(1) if ann_match else ''\n    \n    print(f\"\\n=== CANDIDATE at chr{chrom}:{pos} {ref}>{alt} ===\")\n    print(f\"  ANN: {ann[:200]}\")\n    for s in samples:\n        gt = sample_gts[s]\n        gt_str = f\"{gt[0]}|{gt[1]}\" if gt else \"./.\"\n        marker = \"\"\n        if s in affected:\n            marker = \" [AFFECTED]\"\n        elif s in parents:\n            marker = \" [PARENT]\"\n        elif s in unaffected_siblings:\n            marker = \" [UNAFFECTED]\"\n        elif s in grandparents:\n            marker = \" [GRANDPARENT]\"\n        print(f\"  {s}: {gt_str}{marker}\")\n    \n    candidates.append({\n        'chromosome': chrom,\n        'position': int(pos),\n        'variant_id': vid if vid != '.' else '',\n        'reference': ref,\n        'alternate': alt,\n        'ANN': ann,\n    })\n\nprint(f\"\\n\\nTotal candidates with recessive pattern: {len(candidates)}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Total CFTR region variants: 354\n\n\nTotal candidates with recessive pattern: 0\n</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\n# Step 3 continued: Let's check the genotypes more carefully\n# Maybe the affected samples have ./. (missing) or different patterns\n# Let's look at the first few CFTR variants more closely\n\nimport subprocess\n\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# Extract CFTR region variants\nresult = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} $1==\"7\" && $2>=117120016 && $2<=117308718 {print $1,$2,$4,$5,$9,$10,$11,$12,$13,$14,$15,$16,$17,$18,$19,$20,$21,$22,$23,$24,$25,$26}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines = result.stdout.strip().split('\\n')\nprint(f\"Total CFTR variants: {len(lines)}\")\n\n# Check the genotypes for affected individuals more carefully\nsamples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', \n           'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', \n           'NA12891', 'NA12892', 'NA12893']\n\n# Let me check what GT formats are used and look at all variants where affected share something\nall_records = []\nfor line in lines:\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    chrom = fields[0]\n    pos = fields[1]\n    ref = fields[2]\n    alt = fields[3]\n    fmt = fields[4]  # FORMAT field\n    gts = fields[5:]  # Genotype fields\n    \n    # Just check the first few characters of each GT to understand format\n    affected_gts = gts[2:5]  # NA12879, NA12880, NA12881 correspond to indices 2,3,4\n    all_records.append((int(pos), ref, alt, affected_gts, gts))\n\n# Let's find all variants where affected have 0/1 or 1/1 patterns\nprint(\"\\nScanning for variants with interesting patterns in affected...\")\nfor pos, ref, alt, affected_gts, all_gts in all_records:\n    # Check if all three affected have the same alt allele (either het or hom)\n    gt_set = set()\n    for gt in affected_gts:\n        gt_set.add(gt[:3])  # First 3 chars should be like \"0/1\", \"1/1\", \"0/0\", etc.\n    \n    if len(gt_set) == 1 and list(gt_set)[0] != '0|0' and list(gt_set)[0] != '0/0' and list(gt_set)[0] != '1|.' and list(gt_set)[0] != '.|.' and list(gt_set)[0] != './.':\n        print(f\"Pos {pos}: {ref}>{alt}, Affected GTs: {affected_gts}\")\n\n# Also check wider region around CFTR\nprint(\"\\n\\nChecking all chr7 variants near CFTR with interesting patterns...\")\nresult2 = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} $1==\"7\" && $2>=117000000 && $2<=117400000 {print $1,$2,$4,$5,$10,$11,$12}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines2 = result2.stdout.strip().split('\\n')\nprint(f\"Total chr7:117000000-117400000 variants: {len(lines2)}\")\nfor line in lines2[:20]:\n    fields = line.split('\\t')\n    if len(fields) >= 7:\n        print(f\"  pos={fields[1]}, ref={fields[2]}, alt={fields[3]}, NA12879={fields[4]}, NA12885={fields[5]}, NA12886={fields[6]}\")\n</issue>\n\n<execute>\n# Let me check the GT format and what values we actually have.\n# The samples order is: NA12877 NA12878 NA12879 NA12880 NA12881 NA12882 NA12883 NA12884 NA12885 NA12886 NA12887 NA12888 NA12889 NA12890 NA12891 NA12892 NA12893\n# Affected: NA12879 (idx 2), NA12885 (idx 8), NA12886 (idx 9)\n\nimport subprocess\n\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# Let's get the header to know which column is which\nresult = subprocess.run([\"grep\", \"^#CHROM\", vcf_path], capture_output=True, text=True)\nheader_fields = result.stdout.strip().split('\\t')\nprint(\"Header samples (indices):\")\nfor i, s in enumerate(header_fields[9:]):\n    print(f\"  {i}: {s}\")\n\n# Extract all CFTR exon/intron variants and look at a few\nresult = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} NR>1 && $1==\"7\" && $2>=117120016 && $2<=117308718 {print}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines = result.stdout.strip().split('\\n')\nprint(f\"\\nTotal CFTR variants: {len(lines)}\")\n\n# Check a sample of variants for patterns\nprint(\"\\n=== Checking GT patterns across entire VCF for affected siblings ===\")\nresult3 = subprocess.run([\n    \"awk\",\n    '-F\\t',\n    'BEGIN{OFS=\"\\t\"} NR>1 {print $1,$2,$4,$5,$10,$11,$12}',\n    vcf_path\n], capture_output=True, text=True)\n\n# Read into Python\ngrep_lines = result3.stdout.strip().split('\\n')\nprint(f\"Total variant lines: {len(grep_lines)}\")\n\n# Let's look for variants where affected have 1/1 pattern\ncount = 0\nfor line in grep_lines:\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    if len(fields) >= 7:\n        na12879_gt = fields[4].strip()\n        na12878_gt = fields[5].strip()\n        na12886_gt = fields[6].strip()\n        \n        is_hom_alt = all(gt in ['1/1', '1|1'] for gt in [na12879_gt, na12878_gt, na12886_gt] if gt not in ['.', './.', '.|.'])\n        \n        if is_hom_alt:\n            print(f\"  chr{fields[0]}:{fields[1]} {fields[2]}>{fields[3]} - GT79={na12879_gt}, GT85={na12878_gt}, GT86={na12886_gt}\")\n            count += 1\n\nprint(f\"\\nTotal variants with all affected 1/1: {count}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Error: invalid syntax (<string>, line 68)</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\nimport subprocess\n\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# Get the header to know which column is which\nresult = subprocess.run([\"grep\", \"^#CHROM\", vcf_path], capture_output=True, text=True)\nheader_fields = result.stdout.strip().split('\\t')\nprint(\"Header samples (indices):\")\nfor i, s in enumerate(header_fields[9:]):\n    print(f\"  {i}: {s}\")\n\n# Extract all CFTR variants\nresult = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} NR>1 && $1==\"7\" && $2>=117120016 && $2<=117308718 {print}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines = result.stdout.strip().split('\\n')\nprint(f\"\\nTotal CFTR variants: {len(lines)}\")\n\n# Let me check a few samples to understand the GT format\nprint(\"\\n=== First 10 CFTR variants - GT columns ===\")\nfor i, line in enumerate(lines[:10]):\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    # columns: CHROM POS ID REF ALT QUAL FILTER INFO FORMAT then 17 sample GTs\n    chrom = fields[0]\n    pos = fields[1]\n    ref = fields[3]\n    alt = fields[4]\n    # Sample GTs: indices 9-25\n    na12879_gt = fields[11]  # index 2 in samples\n    na12885_gt = fields[17]  # index 8 in samples  \n    na12886_gt = fields[18]  # index 9 in samples\n    na12877_gt = fields[9]   # index 0 (father)\n    na12878_gt = fields[10]  # index 1 (mother)\n    print(f\"  chr{chrom}:{pos} {ref}>{alt} | GT77={na12877_gt} GT78={na12878_gt} GT79={na12879_gt} GT85={na12885_gt} GT86={na12886_gt}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Header samples (indices):\n  0: NA12877\n  1: NA12878\n  2: NA12879\n  3: NA12880\n  4: NA12881\n  5: NA12882\n  6: NA12883\n  7: NA12884\n  8: NA12885\n  9: NA12886\n  10: NA12887\n  11: NA12888\n  12: NA12889\n  13: NA12890\n  14: NA12891\n  15: NA12892\n  16: NA12893\n\nTotal CFTR variants: 354\n\n=== First 10 CFTR variants - GT columns ===\n  chr7:117122535 A>G | GT77=. GT78=. GT79=. GT85=. GT86=.\n  chr7:117124163 A>C | GT77=. GT78=. GT79=. GT85=. GT86=.\n  chr7:117124798 T>G | GT77=1|0 GT78=. GT79=. GT85=. GT86=.\n  chr7:117125573 A>G | GT77=. GT78=. GT79=. GT85=. GT86=.\n  chr7:117126401 G>A | GT77=1/0 GT78=. GT79=. GT85=. GT86=.\n  chr7:117126950 G>GGAGA | GT77=1/0 GT78=. GT79=. GT85=. GT86=.\n  chr7:117127739 A>C | GT77=1/0 GT78=. GT79=. GT85=. GT86=.\n  chr7:117128942 A>C | GT77=1/1 GT78=. GT79=1|0 GT85=1|0 GT86=1|0\n  chr7:117129404 T>C | GT77=1/1 GT78=. GT79=1|0 GT85=1|0 GT86=1|0\n  chr7:117129491 A>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n</observation>",
+    "================================== Ai Message ==================================\n\n<execute>\nimport subprocess\n\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# Extract all CFTR variants and check GT patterns\nresult = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} NR>1 && $1==\"7\" && $2>=117120016 && $2<=117308718 {print}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines = result.stdout.strip().split('\\n')\nprint(f\"Total CFTR variants: {len(lines)}\")\n\n# Scan for variants where affected are 1/1 or 1|1\naffected_idx = [2, 8, 9]  # NA12879, NA12885, NA12886\nmother_idx = 1  # NA12878\nfather_idx = 0  # NA12877\n\nprint(\"\\n=== Variants where affected are 1/1 or 1|1 ===\")\ncount_affected_hom = 0\nfor line in lines:\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    chrom = fields[0]\n    pos = fields[1]\n    ref = fields[3]\n    alt = fields[4]\n    \n    # Get genotypes for affected\n    gt_79 = fields[11]  # affected_idx[0] + 9\n    gt_85 = fields[17]  # affected_idx[1] + 9\n    gt_86 = fields[18]  # affected_idx[2] + 9\n    \n    gts_affected = [gt_79, gt_85, gt_86]\n    \n    # Check if any affected is 1/1 or 1|1\n    for gt in gts_affected:\n        if gt in ['1/1', '1|1']:\n            gt_father = fields[9]\n            gt_mother = fields[10]\n            print(f\"  chr{chrom}:{pos} {ref}>{alt} | GT77={gt_father} GT78={gt_mother} GT79={gt_79} GT85={gt_85} GT86={gt_86}\")\n            count_affected_hom += 1\n            break\n\nprint(f\"\\nTotal with at least one affected 1/1: {count_affected_hom}\")\n\n# Now also check all non-missing variants among affected\nprint(\"\\n=== All CFTR variants with non-missing GT for all three affected ===\")\ncount_non_missing = 0\nfor line in lines:\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    pos = fields[1]\n    ref = fields[3]\n    alt = fields[4]\n    \n    gt_79 = fields[11]\n    gt_85 = fields[17]\n    gt_86 = fields[18]\n    \n    if gt_79 not in ['.', './.', '.|.'] and gt_85 not in ['.', './.', '.|.'] and gt_86 not in ['.', './.', '.|.']:\n        gt_father = fields[9]\n        gt_mother = fields[10]\n        print(f\"  chr7:{pos} {ref}>{alt} | GT77={gt_father} GT78={gt_mother} GT79={gt_79} GT85={gt_85} GT86={gt_86}\")\n        count_non_missing += 1\n        if count_non_missing >= 30:\n            print(\"  ... (stopping after 30)\")\n            break\n\nprint(f\"\\nTotal with non-missing GT for all affected: {count_non_missing}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Total CFTR variants: 354\n\n=== Variants where affected are 1/1 or 1|1 ===\n  chr7:117149761 T>C | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117223442 TGG>CGC | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117224440 G>A | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117227832 G>T | GT77=1/0 GT78=1/0 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117236235 G>GC | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117238476 G>A | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117256289 C>CT | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117263417 G>C | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117287477 T>C | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117300742 G>A | GT77=1|0 GT78=. GT79=1|1 GT85=. GT86=.\n\nTotal with at least one affected 1/1: 10\n\n=== All CFTR variants with non-missing GT for all three affected ===\n  chr7:117128942 A>C | GT77=1/1 GT78=. GT79=1|0 GT85=1|0 GT86=1|0\n  chr7:117129404 T>C | GT77=1/1 GT78=. GT79=1|0 GT85=1|0 GT86=1|0\n  chr7:117129491 A>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117129562 T>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117132919 A>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117134492 TC>T | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117141835 AA>CA | GT77=1|0 GT78=. GT79=1|. GT85=1|0 GT86=1|.\n  chr7:117147500 C>G | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117149761 T>C | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1\n  chr7:117151828 A>C | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117156341 T>C | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117161653 T>G | GT77=1/1 GT78=. GT79=1|0 GT85=1/0 GT86=1/0\n  chr7:117162140 T>C | GT77=. GT78=1/0 GT79=0|1 GT85=1/0 GT86=1/0\n  chr7:117163047 T>A | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117164439 T>C | GT77=1|0 GT78=. GT79=1/0 GT85=1|0 GT86=1|0\n  chr7:117172221 G>A | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117172269 T>A | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117172731 A>G | GT77=1|0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117173230 T>C | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117175791 T>A | GT77=1|0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117178208 A>G | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117178754 A>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117178807 TA>T | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117181704 AT>A | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117184516 G>A | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117185071 T>C | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117190066 C>T | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117190703 C>T | GT77=1|0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117191283 G>A | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  chr7:117194470 G>A | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0\n  ... (stopping after 30)\n\nTotal with non-missing GT for all affected: 30\n</observation>",
+    "================================== Ai Message ==================================\n\nGood findings! I can see position 7:117227832 stands out with recessive pattern. Let me investigate more systematically.\n\n<execute>\nimport subprocess\nimport re\n\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\n# Extract CFTR region variants\nresult = subprocess.run([\n    \"awk\", \n    '-F\\t', \n    'BEGIN{OFS=\"\\t\"} NR>1 && $1==\"7\" && $2>=117120016 && $2<=117308718 {print}',\n    vcf_path\n], capture_output=True, text=True)\n\nlines = result.stdout.strip().split('\\n')\nprint(f\"Total CFTR variants: {len(lines)}\")\n\n# Find variants with proper recessive pattern: parents 0/1, affected 1/1\nprint(\"\\n=== Variants with proper recessive pattern (parents 0/1, affected 1/1) ===\")\nfor line in lines:\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    chrom = fields[0]\n    pos = int(fields[1])\n    vid = fields[2]\n    ref = fields[3]\n    alt = fields[4]\n    info = fields[7]\n    \n    gt_father = fields[9]   # NA12877\n    gt_mother = fields[10]  # NA12878\n    gt_79 = fields[11]      # NA12879\n    gt_85 = fields[17]      # NA12885\n    gt_86 = fields[18]      # NA12886\n    \n    # Check recessive: parents both 0/1, all three affected 1/1\n    parents_het = gt_father in ['0/1', '0|1'] and gt_mother in ['0/1', '0|1']\n    affected_hom = all(gt in ['1/1', '1|1'] for gt in [gt_79, gt_85, gt_86])\n    \n    if parents_het and affected_hom:\n        # Get ANN info\n        ann_match = re.search(r'ANN=([^\\t]+)', info)\n        ann = ann_match.group(1) if ann_match else ''\n        \n        # Also check unaffected siblings\n        unaffected_gts = []\n        for idx in [3, 4, 5, 6, 7, 10, 11, 16]:  # indices for unaffected siblings\n            gt = fields[idx + 9]\n            unaffected_gts.append(gt)\n        \n        print(f\"\\n=== RECESSIVE CANDIDATE at chr{chrom}:{pos} {ref}>{alt} ===\")\n        print(f\"  ID: {vid}\")\n        print(f\"  Father: {gt_father}, Mother: {gt_mother}\")\n        print(f\"  Affected: 79={gt_79}, 85={gt_85}, 86={gt_86}\")\n        print(f\"  Unaffected siblings: {unaffected_gts}\")\n        print(f\"  ANN: {ann}\")\n        \n# Also check: what about chr7:117227832 more carefully?\nprint(\"\\n\\n=== Details for chr7:117227832 ===\")\nfor line in lines:\n    if not line.strip():\n        continue\n    fields = line.split('\\t')\n    if fields[1] == '117227832':\n        info = fields[7]\n        ann_match = re.search(r'ANN=([^\\t]+)', info)\n        ann = ann_match.group(1) if ann_match else ''\n        print(f\"Full record:\")\n        print(f\"  POS: {fields[1]}\")\n        print(f\"  REF: {fields[3]}, ALT: {fields[4]}\")\n        print(f\"  INFO field: {info[:500]}\")\n        print(f\"  ANN: {ann}\")\n        \n        # Print all genotypes\n        samples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', \n                   'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', \n                   'NA12891', 'NA12892', 'NA12893']\n        for i, s in enumerate(samples):\n            print(f\"  {s}: {fields[i+9]}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Total CFTR variants: 354\n\n=== Variants with proper recessive pattern (parents 0/1, affected 1/1) ===\n\n\n=== Details for chr7:117227832 ===\nFull record:\n  POS: 117227832\n  REF: G, ALT: T\n  INFO field: AC=14;AN=22;ANN=T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|12/27|c.1624G>T|p.Gly542*|1756/6128|1624/4443|542/1480||,T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|11/26|c.1441G>T|p.Gly481*|1573/5949|1441/4260|481/1419||,T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|11/26|c.1534G>T|p.Gly512*|1534/4316|1534/4316|512/1437||WARNING_TRANSCRIPT_INCOMPLETE,T|upstream_gene_variant|MODIFIER|AC000\n  ANN: T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|12/27|c.1624G>T|p.Gly542*|1756/6128|1624/4443|542/1480||,T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|11/26|c.1441G>T|p.Gly481*|1573/5949|1441/4260|481/1419||,T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|11/26|c.1534G>T|p.Gly512*|1534/4316|1534/4316|512/1437||WARNING_TRANSCRIPT_INCOMPLETE,T|upstream_gene_variant|MODIFIER|AC000111.5|ENSG00000234001|transcript|ENST00000448200|processed_pseudogene||n.-1362C>A|||||1362|,T|downstream_gene_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000472848|processed_transcript||n.*29G>T|||||29|;LOF=(CFTR|ENSG00000001626|11|0.27);NMD=(CFTR|ENSG00000001626|11|0.27)\n  NA12877: 1/0\n  NA12878: 1/0\n  NA12879: 1/1\n  NA12880: .\n  NA12881: .\n  NA12882: 1/0\n  NA12883: 1/0\n  NA12884: 1/0\n  NA12885: 1/1\n  NA12886: 1/1\n  NA12887: .\n  NA12888: 1/0\n  NA12889: 1/0\n  NA12890: .\n  NA12891: .\n  NA12892: 1/0\n  NA12893: .\n</observation>",
+    "================================== Ai Message ==================================\n\nFound it! The variant at chr7:117227832 (G>T, causing p.Gly542* stop-gained in CFTR) shows perfect recessive inheritance. Now let me cross-reference with ClinVar.\n\n<execute>\n# Step 4: Cross-reference with ClinVar VCF\nimport subprocess\nimport re\n\nclinvar_vcf = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference/clinvar_20250521.vcf.gz\"\n\n# Check chromosome naming in ClinVar - it might use \"NC_000007.13\" or just \"7\"\n# Let's look at a few variants to understand the format\nresult = subprocess.run([\"gunzip\", \"-c\", clinvar_vcf, \"|\", \"head\", \"-50\"], capture_output=True, text=True, shell=True)\nprint(result.stdout[:2000])\n\n# Check if tabix index exists\nresult2 = subprocess.run([\"ls\", \"-la\", clinvar_vcf.replace(\".gz\", \"\")], capture_output=True, text=True)\nprint(f\"\\nUnzipped file: {result2.stdout}\")\n\n# Use bcftools or tabix to query the region\nresult3 = subprocess.run([\"tabix\", clinvar_vcf, \"7:117227832-117227832\"], capture_output=True, text=True)\nprint(f\"\\nTabix query result:\")\nprint(result3.stdout)\nprint(f\"Stderr: {result3.stderr}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>\n\nUnzipped file: \n\nTabix query result:\n7\t117227790\t634914\tTAGGACATCTCCAAGTTTGCAGAGAAAGACAATATAGTTCTTGGAGAAGGTGGAATCACACTGAGTGGAGGTCAACGAGCAAGAATTTCTTTAGCA\tT\t.\t.\tALLELEID=622747;CLNDISDB=MONDO:MONDO:0009061,MedGen:C0010674,OMIM:219700,Orphanet:586;CLNDN=Cystic_fibrosis;CLNHGVS=NC_000007.13:g.117227794_117227888del;CLNREVSTAT=criteria_provided,_single_submitter;CLNSIG=Pathogenic;CLNSIGSCV=SCV000924336;CLNVC=Deletion;CLNVCSO=SO:0000159;CLNVI=ClinGen:CA913190192;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001574|splice_acceptor_variant,SO:0001575|splice_donor_variant;ORIGIN=0;RS=1562906265\n7\t117227831\t1434498\tTG\tT\t.\t.\tALLELEID=1494314;CLNDISDB=MONDO:MONDO:0009061,MedGen:C0010674,OMIM:219700,Orphanet:586;CLNDN=Cystic_fibrosis;CLNHGVS=NC_000007.13:g.117227833del;CLNREVSTAT=criteria_provided,_single_submitter;CLNSIG=Pathogenic;CLNSIGSCV=SCV002209119;CLNVC=Deletion;CLNVCSO=SO:0000159;CLNVI=ClinGen:CA2573141659;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001589|frameshift_variant;ORIGIN=1;RS=2116017718\n7\t117227832\t3233783\tG\tA\t.\t.\tALLELEID=3392906;CLNDISDB=MedGen:CN169374;CLNDN=not_specified;CLNHGVS=NC_000007.13:g.117227832G>A;CLNREVSTAT=criteria_provided,_single_submitter;CLNSIG=Uncertain_significance;CLNSIGSCV=SCV005039160;CLNVC=single_nucleotide_variant;CLNVCSO=SO:0001483;CLNVI=ClinGen:CA368975971;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001583|missense_variant;ORIGIN=1;RS=113993959\n7\t117227832\t7115\tG\tT\t.\t.\tAF_EXAC=0.00026;ALLELEID=22154;CLNDISDB=MONDO:MONDO:0009061,MedGen:C0010674,OMIM:219700,Orphanet:586|MONDO:MONDO:0010178,MedGen:C0403814,OMIM:277180,Orphanet:48|MONDO:MONDO:0008185,MedGen:C0238339,OMIM:167800,Orphanet:676|MONDO:MONDO:0008887,MedGen:C2749757,OMIM:211400,Orphanet:60033|MedGen:C5924204|MedGen:C3661900;CLNDN=Cystic_fibrosis|Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation|Hereditary_pancreatitis|Bronchiectasis_with_or_without_elevated_sweat_chloride_1|CFTR-related_disorder|not_provided;CLNHGVS=NC_000007.13:g.117227832G>T;CLNREVSTAT=practice_guideline;CLNSIG=Pathogenic;CLNSIGSCV=SCV000071394;CLNVC=single_nucleotide_variant;CLNVCSO=SO:0001483;CLNVI=ClinGen:CA284836|Genetic_Testing_Registry_(GTR):GTR000028916|Genetic_Testing_Registry_(GTR):GTR000074114|Genetic_Testing_Registry_(GTR):GTR000500233|Genetic_Testing_Registry_(GTR):GTR000576392|OMIM:602421.0009|OMIM:602421.0095;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001587|nonsense;ORIGIN=13;RS=113993959\n\nStderr: \n</observation>",
+    "================================== Ai Message ==================================\n\nExcellent! I found the causal variant. Let me now build the complete output CSV.\n\n<execute>\nimport subprocess\nimport re\n\n# The causal variant: chr7:117227832 G>T, CFTR c.1624G>T p.Gly542* (G542X)\n\n# Verify all genotypes one more time to be thorough\nvcf_path = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf\"\n\nresult = subprocess.run([\n    \"awk\",\n    '-F\\t',\n    'BEGIN{OFS=\"\\t\"} NR>1 && $1==\"7\" && $2>=117227832 && $2<=117227832 {print}',\n    vcf_path\n], capture_output=True, text=True)\n\nline = result.stdout.strip()\nfields = line.split('\\t')\n\nsamples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', \n           'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', \n           'NA12891', 'NA12892', 'NA12893']\n\nprint(\"Genotype summary for chr7:117227832 G>T (CFTR c.1624G>T p.Gly542*):\")\nfor i, s in enumerate(samples):\n    gt = fields[i+9]\n    role = \"\"\n    if s in ['NA12877']: role = \"(Father)\"\n    elif s in ['NA12878']: role = \"(Mother)\"\n    elif s in ['NA12879', 'NA12885', 'NA12886']: role = \"(AFFECTED)\"\n    elif s in ['NA12889', 'NA12890', 'NA12891', 'NA12892']: role = \"(Grandparent)\"\n    else: role = \"(Sibling)\"\n    \n    interpretation = \"\"\n    if gt in ['1/1', '1|1']:\n        interpretation = \"Homozygous alt - affected\"\n    elif gt in ['0/1', '1/0', '0|1', '1|0']:\n        interpretation = \"Heterozygous carrier\"\n    elif gt in ['0/0', '0|0']:\n        interpretation = \"Homozygous ref - unaffected\"\n    else:\n        interpretation = f\"No call ({gt})\"\n    \n    print(f\"  {s:12s} {role:15s} GT={gt:5s}  {interpretation}\")\n\nprint(\"\\n\\nThis confirms recessive inheritance pattern:\")\nprint(\"  - Both parents are carriers (0/1)\")\nprint(\"  - All three affected siblings are homozygous for the mutation (1/1)\")\nprint(\"  - Unaffected siblings are either carriers (0/1) or have no call\")\n\n# Now cross-reference with ClinVar for the exact variant\nclinvar_vcf = \"/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference/clinvar_20250521.vcf.gz\"\nresult2 = subprocess.run([\"tabix\", clinvar_vcf, \"7:117227832-117227832\"], capture_output=True, text=True)\n\n# Parse the ClinVar record for the G>T variant specifically\nfor cline in result2.stdout.strip().split('\\n'):\n    if not cline:\n        continue\n    clfields = cline.split('\\t')\n    ref = clfields[3]\n    alt = clfields[4]\n    info = clfields[7]\n    \n    if ref == 'G' and alt == 'T':\n        print(f\"\\nClinVar record for G>T:\")\n        print(f\"  Position: {clfields[1]}\")\n        print(f\"  RS ID: {clfields[2]}\")\n        print(f\"  INFO: {info}\")\n        \n        # Parse key ClinVar fields\n        info_parts = info.split(';')\n        clin_parsed = {}\n        for part in info_parts:\n            if '=' in part:\n                key, val = part.split('=', 1)\n                clin_parsed[key] = val\n        \n        print(f\"\\n  CLNSIG: {clin_parsed.get('CLNSIG', 'N/A')}\")\n        print(f\"  CLNDN: {clin_parsed.get('CLNDN', 'N/A')}\")\n        print(f\"  CLNREVSTAT: {clin_parsed.get('CLNREVSTAT', 'N/A')}\")\n        print(f\"  RS: {clfields[2]}\")\n        print(f\"  GENE: {clin_parsed.get('GENEINFO', 'N/A')}\")\n        print(f\"  CLNHGVS: {clin_parsed.get('CLNHGVS', 'N/A')}\")\n        print(f\"  MC: {clin_parsed.get('MC', 'N/A')}\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>Genotype summary for chr7:117227832 G>T (CFTR c.1624G>T p.Gly542*):\n  NA12877      (Father)        GT=1/0    Heterozygous carrier\n  NA12878      (Mother)        GT=1/0    Heterozygous carrier\n  NA12879      (AFFECTED)      GT=1/1    Homozygous alt - affected\n  NA12880      (Sibling)       GT=.      No call (.)\n  NA12881      (Sibling)       GT=.      No call (.)\n  NA12882      (Sibling)       GT=1/0    Heterozygous carrier\n  NA12883      (Sibling)       GT=1/0    Heterozygous carrier\n  NA12884      (Sibling)       GT=1/0    Heterozygous carrier\n  NA12885      (AFFECTED)      GT=1/1    Homozygous alt - affected\n  NA12886      (AFFECTED)      GT=1/1    Homozygous alt - affected\n  NA12887      (Sibling)       GT=.      No call (.)\n  NA12888      (Sibling)       GT=1/0    Heterozygous carrier\n  NA12889      (Grandparent)   GT=1/0    Heterozygous carrier\n  NA12890      (Grandparent)   GT=.      No call (.)\n  NA12891      (Grandparent)   GT=.      No call (.)\n  NA12892      (Grandparent)   GT=1/0    Heterozygous carrier\n  NA12893      (Sibling)       GT=.      No call (.)\n\n\nThis confirms recessive inheritance pattern:\n  - Both parents are carriers (0/1)\n  - All three affected siblings are homozygous for the mutation (1/1)\n  - Unaffected siblings are either carriers (0/1) or have no call\n\nClinVar record for G>T:\n  Position: 117227832\n  RS ID: 7115\n  INFO: AF_EXAC=0.00026;ALLELEID=22154;CLNDISDB=MONDO:MONDO:0009061,MedGen:C0010674,OMIM:219700,Orphanet:586|MONDO:MONDO:0010178,MedGen:C0403814,OMIM:277180,Orphanet:48|MONDO:MONDO:0008185,MedGen:C0238339,OMIM:167800,Orphanet:676|MONDO:MONDO:0008887,MedGen:C2749757,OMIM:211400,Orphanet:60033|MedGen:C5924204|MedGen:C3661900;CLNDN=Cystic_fibrosis|Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation|Hereditary_pancreatitis|Bronchiectasis_with_or_without_elevated_sweat_chloride_1|CFTR-related_disorder|not_provided;CLNHGVS=NC_000007.13:g.117227832G>T;CLNREVSTAT=practice_guideline;CLNSIG=Pathogenic;CLNSIGSCV=SCV000071394;CLNVC=single_nucleotide_variant;CLNVCSO=SO:0001483;CLNVI=ClinGen:CA284836|Genetic_Testing_Registry_(GTR):GTR000028916|Genetic_Testing_Registry_(GTR):GTR000074114|Genetic_Testing_Registry_(GTR):GTR000500233|Genetic_Testing_Registry_(GTR):GTR000576392|OMIM:602421.0009|OMIM:602421.0095;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001587|nonsense;ORIGIN=13;RS=113993959\n\n  CLNSIG: Pathogenic\n  CLNDN: Cystic_fibrosis|Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation|Hereditary_pancreatitis|Bronchiectasis_with_or_without_elevated_sweat_chloride_1|CFTR-related_disorder|not_provided\n  CLNREVSTAT: practice_guideline\n  RS: 7115\n  GENE: CFTR:1080|LOC111674475:111674475\n  CLNHGVS: NC_000007.13:g.117227832G>T\n  MC: SO:0001587|nonsense\n</observation>",
+    "================================== Ai Message ==================================\n\nExcellent! Now I have all the information needed. Let me construct the final CSV output.\n\n<execute>\n# Step 5: Build and save the final CSV\nimport csv\nimport os\n\n# The causal variant data\noutput_path = \"/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv\"\n\nrow = {\n    'chromosome': '7',\n    'position': '117227832',\n    'variant_id': '7115',\n    'reference': 'G',\n    'alternate': 'T',\n    'gene_name': 'CFTR',\n    'gene_id': 'ENSG00000001626',\n    'annotation': 'stop_gained',\n    'impact': 'HIGH',\n    'transcript_id': 'ENST00000003084',\n    'hgvs_c': 'c.1624G>T',\n    'hgvs_p': 'p.Gly542*',\n    'clinical_significance': 'Pathogenic',\n    'diseases': 'Cystic_fibrosis; Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation; Hereditary_pancreatitis; Bronchiectasis_with_or_without_elevated_sweat_chloride_1; CFTR-related_disorder; not_provided',\n    'review_status': 'practice_guideline',\n    'rs_id': 'rs113993959'\n}\n\n# Write CSV\nfieldnames = ['chromosome', 'position', 'variant_id', 'reference', 'alternate', \n              'gene_name', 'gene_id', 'annotation', 'impact', 'transcript_id',\n              'hgvs_c', 'hgvs_p', 'clinical_significance', 'diseases', \n              'review_status', 'rs_id']\n\nwith open(output_path, 'w', newline='') as f:\n    writer = csv.DictWriter(f, fieldnames=fieldnames)\n    writer.writeheader()\n    writer.writerow(row)\n\nprint(f\"CSV written to {output_path}\")\nprint(\"\\nContents:\")\nwith open(output_path, 'r') as f:\n    print(f.read())\n\n# Also verify the file exists and is readable\nprint(f\"File exists: {os.path.exists(output_path)}\")\nprint(f\"File size: {os.path.getsize(output_path)} bytes\")\n</execute>",
+    "================================== Ai Message ==================================\n\n<observation>CSV written to /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv\n\nContents:\nchromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id\n7,117227832,7115,G,T,CFTR,ENSG00000001626,stop_gained,HIGH,ENST00000003084,c.1624G>T,p.Gly542*,Pathogenic,Cystic_fibrosis; Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation; Hereditary_pancreatitis; Bronchiectasis_with_or_without_elevated_sweat_chloride_1; CFTR-related_disorder; not_provided,practice_guideline,rs113993959\n\nFile exists: True\nFile size: 506 bytes\n</observation>",
+    "================================== Ai Message ==================================\n\nNow let me update the plan and provide the final summary.\n\n1. [✓] Read the family description to understand the pedigree and affected individuals\n2. [✓] Examine the family VCF (ex1.eff.vcf) to understand its structure\n3. [✓] Filter for variants in affected siblings (NA12885, NA12886, NA12879) that follow recessive inheritance pattern\n4. [✓] Cross-reference with ClinVar VCF to find CFTR variants with clinical significance\n5. [✓] Identify the causal recessive variant consistent with affected siblings\n6. [✓] Format and save the output CSV\n\n## Final Summary\n\n**Task**: Identify the causal recessive variant for Cystic Fibrosis in affected siblings NA12879, NA12885, and NA12886.\n\n**Approach**:\n1. Analyzed the family pedigree (2 unaffected carrier parents, 11 siblings including 3 affected)\n2. Scanned 354 variants in the CFTR gene region (chr7:117,120,016-117,308,718) from the annotated VCF\n3. Applied recessive inheritance filter (parents both heterozygous 0/1, affected siblings homozygous alt 1/1, unaffected siblings not 1/1)\n4. Cross-referenced candidates with the ClinVar VCF\n\n**Causal Variant Identified**:\n- **Position**: chr7:117227832\n- **Variant**: G>T (rs113993959, ClinVar ID 7115)\n- **Gene**: CFTR (ENSG00000001626)\n- **Coding Change**: c.1624G>T (ENST00000003084)\n- **Protein Change**: p.Gly542* (G542X) - stop_gained/nonsense mutation\n- **Clinical Significance**: Pathogenic (practice guideline reviewed)\n- **Disease**: Cystic fibrosis (and associated conditions)\n\n**Inheritance Evidence**:\n- Both parents (NA12877, NA12878) are heterozygous carriers (0/1)\n- All three affected siblings (NA12879, NA12885, NA12886) are homozygous (1/1)\n- Unaffected siblings are either carriers (0/1) or have no-call, consistent with recessive inheritance\n\n**Output file**: `/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv`\n\n<solution>\nThe answer is: The causal recessive CFTR variant is chr7:117227832 G>T (rs113993959), a stop-gained mutation (c.1624G>T, p.Gly542*) with HIGH impact, classified as Pathogenic with practice guideline review status, associated with Cystic fibrosis. The CSV has been saved to the required output path.\n</solution>"
+  ]
+}

Biomni/experiments/bioagent_bench/runs/scale_100/cystic-fibrosis_20260514_192139/execution_log.txt

@@ -0,0 +1,1360 @@
+================================ Human Message =================================
+
+You are running a bioagent-bench task with local files already prepared.
+
+Task ID: cystic-fibrosis
+Task name: Cystic Fibrosis Mendelian Variant Identification
+Benchmark prompt:
+Find the genetic cause of Cystic fibrosis; identify the causal recessive variant consistent with affected siblings NA12885, NA12886, and NA12879. The output should be a CSV file with the following columns: chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id. <example>chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id
+X,123456789,VAR123,A,G,GENE1,ENSG00000000001,missense_variant,MODERATE,ENST00000000001,c.123A>G,p.Lys41Arg,Likely_pathogenic,Disease_A; Disease_B; not_provided,reviewed_by_expert_panel,rs0000001</example>
+Data background:
+The sample dataset is a simulated dataset for finding the genetic cause of Cystic fibrosis. The dataset is real sequencing data from CEPH_1463 dataset provided by the Complete Genomics Diversity Panel. It consists of sequencing of a family: 4 grandparents, 2 parents and 11 siblings. A known Mandelian disease mutation has been added on three siblings, taking care to be consistent with the underlying heplotype structure. The goal is to find the mutation causing the mendalian recessive trait - Cystic Fibrosis.
+
+Constraints:
+1. Use only the benchmark inputs and references explicitly listed below.
+2. Save the required final deliverables exactly to the paths listed below.
+3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139
+4. Keep final deliverables in the same schema/format requested by the benchmark prompt.
+5. Return a concise final summary after writing the required files.
+
+Task-specific instruction:
+Use only the provided family variant data and ClinVar VCF. The final row should identify the causal CFTR recessive variant consistent with affected siblings.
+
+Benchmark data policy:
+- Allowed input data directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/data
+- Allowed reference directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference
+- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139
+- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/results
+- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than cystic-fibrosis>
+- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.
+- Do not download external databases or install new packages during the benchmark run.
+
+Input data directory:
+/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data
+Visible input files:
+- ex1.eff.vcf
+- family_description.txt
+
+Reference data directory:
+/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference
+Visible reference files:
+- clinvar_20250521.vcf.gz
+- clinvar_20250521.vcf.gz.tbi
+
+Required final output paths:
+- cf_variants.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv
+
+================================== Ai Message ==================================
+
+I'll start by examining the input data files and understanding the family structure, then identify the causal CFTR variant.
+
+## Plan
+
+1. [ ] Read the family description to understand the pedigree and affected individuals
+2. [ ] Examine the family VCF (ex1.eff.vcf) to understand its structure
+3. [ ] Filter for variants in affected siblings (NA12885, NA12886, NA12879) that follow recessive inheritance pattern
+4. [ ] Cross-reference with ClinVar VCF to find CFTR variants with clinical significance
+5. [ ] Identify the causal recessive variant consistent with affected siblings
+6. [ ] Format and save the output CSV
+
+Let me start with step 1.
+
+<execute>
+# Step 1: Read the family description
+with open("/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/family_description.txt", "r") as f:
+    print(f.read())
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>This is a three-generation family with the following structure:
+
+First Generation (Grandparents):
+- Paternal grandfather: NA12889 (unaffected male)
+- Paternal grandmother: NA12890 (unaffected female)
+- Maternal grandfather: NA12891 (unaffected male)
+- Maternal grandmother: NA12892 (unaffected female)
+
+Second Generation (Parents):
+- Father: NA12877 (unaffected male, child of NA12889 and NA12890)
+- Mother: NA12878 (unaffected female, child of NA12891 and NA12892)
+
+Third Generation (Children of NA12877 and NA12878):
+1. NA12879 (affected female)
+2. NA12880 (unaffected female)
+3. NA12881 (unaffected female)
+4. NA12882 (unaffected male)
+5. NA12883 (unaffected male)
+6. NA12884 (unaffected male)
+7. NA12885 (affected female)
+8. NA12886 (affected male)
+9. NA12887 (unaffected female)
+10. NA12888 (unaffected male)
+11. NA12893 (unaffected male)
+</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+# Step 2: Examine the VCF file structure
+import subprocess
+# Check the header and first few lines
+result = subprocess.run(["head", "-n", "50", "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"], capture_output=True, text=True)
+print(result.stdout)
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>##fileformat=VCFv4.1
+##fileDate=20120730
+##center=Complete Genomics
+##source=CGAPipeline_2.0.0.26;cgatools_1.6.0
+##source_GENOME_REFERENCE=NCBI build 37
+##phasing=partial
+##ALT=<ID=CGA_NOCALL,Description="No-called record">
+##ALT=<ID=CGA_CNVWIN,Description="Copy number analysis window">
+##ALT=<ID=INS:ME:ALU,Description="Insertion of ALU element">
+##ALT=<ID=INS:ME:L1,Description="Insertion of L1 element">
+##ALT=<ID=INS:ME:SVA,Description="Insertion of SVA element">
+##ALT=<ID=INS:ME:MER,Description="Insertion of MER element">
+##ALT=<ID=INS:ME:LTR,Description="Insertion of LTR element">
+##ALT=<ID=INS:ME:PolyA,Description="Insertion of PolyA element">
+##ALT=<ID=INS:ME:HERV,Description="Insertion of HERV element">
+##FILTER=<ID=VQLOW,Description="Quality not VQHIGH">
+##FILTER=<ID=SQLOW,Description="Somatic quality not SQHIGH">
+##FILTER=<ID=URR,Description="Too close to an underrepresented repeat">
+##FILTER=<ID=MPCBT,Description="Mate pair count below 10">
+##FILTER=<ID=SHORT,Description="Junction side length below 70">
+##FILTER=<ID=TSNR,Description="Transition sequence not resolved">
+##FILTER=<ID=INTERBL,Description="Interchromosomal junction in baseline">
+##FILTER=<ID=sns75,Description="Sensitivity to known MEI calls in range (.75,.95] i.e. medium FDR">
+##FILTER=<ID=sns95,Description="Sensitivity to known MEI calls in range (.95,1.00] i.e. high to very high FDR">
+##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
+##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">
+##INFO=<ID=IMPRECISE,Number=0,Type=Flag,Description="Imprecise structural variation">
+##INFO=<ID=SVLEN,Number=.,Type=Integer,Description="Difference in length between REF and ALT alleles">
+##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
+##source_20130711.1=vcf-merge(r840) --collapse any --remove-duplicates --trim-ALTs --regions 1 /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12877.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12878.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12879.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12880.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12881.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12882.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12883.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12884.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12885.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12886.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12887.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12888.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12889.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12890.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12891.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12892.vcf.gz /wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12893.vcf.gz
+##sourceFiles_20130711.1=0:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12877.vcf.gz,1:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12878.vcf.gz,2:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12879.vcf.gz,3:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12880.vcf.gz,4:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12881.vcf.gz,5:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12882.vcf.gz,6:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12883.vcf.gz,7:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12884.vcf.gz,8:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12885.vcf.gz,9:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12886.vcf.gz,10:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12887.vcf.gz,11:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12888.vcf.gz,12:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12889.vcf.gz,13:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12890.vcf.gz,14:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12891.vcf.gz,15:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12892.vcf.gz,16:/wsu/home/eq/eq83/eq8302/cg_panel/ceph/vcfBeta-NA12893.vcf.gz
+##INFO=<ID=SF,Number=.,Type=String,Description="Source File (index to sourceFiles, f when filtered)">
+##INFO=<ID=AC,Number=.,Type=Integer,Description="Allele count in genotypes">
+##INFO=<ID=AN,Number=1,Type=Integer,Description="Total number of alleles in called genotypes">
+##SnpEffVersion="5.2 (build 2023-09-29 06:17), by Pablo Cingolani"
+##SnpEffCmd="SnpEff  -lof GRCh37.75 /home/dev/bioagent-bench/tasks/fibrosis/data/protocols/ex1.vcf "
+##INFO=<ID=ANN,Number=.,Type=String,Description="Functional annotations: 'Allele | Annotation | Annotation_Impact | Gene_Name | Gene_ID | Feature_Type | Feature_ID | Transcript_BioType | Rank | HGVS.c | HGVS.p | cDNA.pos / cDNA.length | CDS.pos / CDS.length | AA.pos / AA.length | Distance | ERRORS / WARNINGS / INFO' ">
+##INFO=<ID=LOF,Number=.,Type=String,Description="Predicted loss of function effects for this variant. Format: 'Gene_Name | Gene_ID | Number_of_transcripts_in_gene | Percent_of_transcripts_affected'">
+##INFO=<ID=NMD,Number=.,Type=String,Description="Predicted nonsense mediated decay effects for this variant. Format: 'Gene_Name | Gene_ID | Number_of_transcripts_in_gene | Percent_of_transcripts_affected'">
+#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	NA12877	NA12878	NA12879	NA12880	NA12881	NA12882	NA12883	NA12884	NA12885	NA12886	NA12887	NA12888	NA12889	NA12890	NA12891	NA12892	NA12893
+7	16487	.	G	T	.	.	AC=9;AN=9;ANN=T|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16487G>T||||||	GT	1|1	.	.	.	.	1|1	.	.	.	.	.	1|.	.	1|1	1|1	.	.
+7	16671	.	T	C	.	.	AC=2;AN=2;ANN=C|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16671T>C||||||	GT	.	.	.	.	.	.	.	.	.	.	.	1|.	1|.	.	.	.	.
+7	16692	.	G	C	.	.	AC=6;AN=6;ANN=C|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16692G>C||||||	GT	.	.	.	.	.	.	.	.	.	.	.	1|.	1|1	.	1|.	1/1	.
+7	16712	.	T	G	.	.	AC=1;AN=2;ANN=G|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16712T>G||||||	GT	.	.	.	.	.	.	.	.	.	.	.	.	0|1	.	.	.	.
+7	16717	.	CTG	ATA	.	.	AC=1;AN=1;ANN=ATA|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16717_16719delCTGinsATA||||||	GT	.	.	.	.	.	.	.	.	.	.	.	.	.|1	.	.	.	.
+7	16719	.	G	A	.	.	AC=2;AN=2;ANN=A|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16719G>A||||||	GT	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1/1	.
+7	16787	.	AAATCCG	A	.	.	AC=1;AN=2;ANN=A|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16788_16793delAATCCG||||||	GT	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1|0	.
+7	16798	.	T	C	.	.	AC=1;AN=2;ANN=C|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16798T>C||||||	GT	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1|0	.
+7	16878	.	A	T	.	.	AC=1;AN=1;ANN=T|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.16878A>T||||||	GT	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.|1	.
+7	17881	.	T	TCAA	.	.	AC=2;AN=2;ANN=TCAA|intergenic_region|MODIFIER|CHR_START-AC093627.7|CHR_START-ENSG00000232325|intergenic_region|CHR_START-ENSG00000232325|||n.17881_17882insCAA||||||	GT	.	.	.	.	.	.	.	.	.	.	.	.	.	1/1	.	.	.
+
+</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+# Step 2 continued: Understand the VCF structure - check column headers and more data
+import subprocess
+
+# Check the column names (samples) from the VCF header
+result = subprocess.run(["grep", "^#CHROM", "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"], capture_output=True, text=True)
+print("Column headers:")
+print(result.stdout)
+
+# Let me also count total lines
+result2 = subprocess.run(["wc", "-l", "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"], capture_output=True, text=True)
+print(f"\nTotal lines: {result2.stdout}")
+
+# Check what chromosomes are present
+result3 = subprocess.run(["grep", "-v", "^#", "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"], capture_output=True, text=True)
+import pandas as pd
+import io
+# Parse just the first columns to check chromosomes
+lines = result3.stdout.strip().split('\n')
+chromes = set()
+for line in lines[:1000]:
+    if line:
+        chromes.add(line.split('\t')[0])
+print(f"\nChromosomes found (first 1000 lines): {sorted(chromes)}")
+print(f"Total variant lines: {len(lines)}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Column headers:
+#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	NA12877	NA12878	NA12879	NA12880	NA12881	NA12882	NA12883	NA12884	NA12885	NA12886	NA12887	NA12888	NA12889	NA12890	NA12891	NA12892	NA12893
+
+
+Total lines: 743194 /225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf
+
+
+Chromosomes found (first 1000 lines): ['7']
+Total variant lines: 743154
+</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+# Step 3: Find CFTR variants in the VCF with recessive inheritance pattern
+# CFTR gene location on GRCh37: chr7:117,120,016-117,308,718
+
+import subprocess
+import pandas as pd
+import io
+
+# Use cyvcf2 for efficient VCF parsing
+from cyvcf2 import VCF
+
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# Affected siblings
+affected = ['NA12879', 'NA12885', 'NA12886']
+# Parents (should be carriers - heterozygous)
+parents = ['NA12877', 'NA12878']
+# All samples list from the VCF header
+all_samples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', 
+               'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', 
+               'NA12891', 'NA12892', 'NA12893']
+# Unaffected siblings (not affected, not parents)
+unaffected = ['NA12880', 'NA12881', 'NA12882', 'NA12883', 'NA12884', 'NA12887', 'NA12888', 'NA12893']
+# Grandparents
+grandparents = ['NA12889', 'NA12890', 'NA12891', 'NA12892']
+
+# Open VCF
+vcf = VCF(vcf_path)
+
+# Focus on CFTR region
+cftr_chrom = '7'
+cftr_start = 117120016
+cftr_end = 117308718
+
+print("Searching for variants in CFTR gene region...")
+print(f"CFTR region: chr{cftr_chrom}:{cftr_start}-{cftr_end}")
+
+candidate_variants = []
+
+for variant in vcf(f'{cftr_chrom}:{cftr_start}-{cftr_end}'):
+    # Get genotypes for all samples
+    genotypes = variant.genotypes
+    
+    # For recessive: affected should be homozygous alt (1/1)
+    affected_hom_alt = all(
+        genotypes[all_samples.index(s)][0] == 1 and 
+        genotypes[all_samples.index(s)][1] == 1 and
+        genotypes[all_samples.index(s)][0] == genotypes[all_samples.index(s)][1]
+        for s in affected
+    )
+    
+    if not affected_hom_alt:
+        continue
+    
+    # Parents should be heterozygous (0/1)
+    parents_het = all(
+        genotypes[all_samples.index(s)][0] == 0 and 
+        genotypes[all_samples.index(s)][1] == 1
+        for s in parents
+    )
+    
+    if not parents_het:
+        continue
+    
+    # Check if unaffected siblings are not homozygous alt
+    unaffected_not_hom_alt = all(
+        not (genotypes[all_samples.index(s)][0] == 1 and 
+             genotypes[all_samples.index(s)][1] == 1)
+        for s in unaffected
+    )
+    
+    if not unaffected_not_hom_alt:
+        continue
+    
+    # Get annotation info
+    ann = variant.INFO.get('ANN', '')
+    
+    candidate = {
+        'chromosome': variant.CHROM,
+        'position': variant.POS,
+        'variant_id': variant.ID if variant.ID else '.',
+        'reference': variant.REF,
+        'alternate': ','.join(variant.ALT),
+        'ANN': ann,
+        'qual': variant.QUAL,
+        'filter': variant.FILTER,
+    }
+    
+    # Also print genotypes for all relevant samples
+    print(f"\nFound candidate at chr{variant.CHROM}:{variant.POS} {variant.REF}>{','.join(variant.ALT)}")
+    for s in all_samples:
+        idx = all_samples.index(s)
+        gt = f"{genotypes[idx][0]}|{genotypes[idx][1]}"
+        marker = ""
+        if s in affected:
+            marker = " [AFFECTED]"
+        elif s in parents:
+            marker = " [PARENT]"
+        elif s in unaffected:
+            marker = " [UNAFFECTED]"
+        print(f"  {s}: {gt}{marker}")
+    
+    candidate_variants.append(candidate)
+
+vcf.close()
+
+print(f"\n\nTotal candidates with recessive pattern: {len(candidate_variants)}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Error: No module named 'cyvcf2'</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+# Step 3 alternative: Use command-line tools to parse the VCF file
+import subprocess
+import pandas as pd
+import io
+
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# First, let's check what's on chromosome 7 around the CFTR gene region
+# CFTR on GRCh37: chr7:117,120,016-117,308,718
+
+# Let's use awk to extract variants in CFTR region from the VCF
+result = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} $1=="7" && $2>=117120016 && $2<=117308718 {print}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines = result.stdout.strip().split('\n')
+print(f"Number of variants in CFTR region: {len(lines)}")
+if len(lines) > 0:
+    for line in lines[:5]:
+        print(line)
+else:
+    print("No variants found in that range. Let me check what variants are on chromosome 7...")
+    
+# Let me check general chromosome 7 variants
+result2 = subprocess.run([
+    "awk", 
+    '-F\t', 
+    '$1=="7" {print $1, $2, $4, $5}',
+    vcf_path
+], capture_output=True, text=True)
+lines2 = result2.stdout.strip().split('\n')
+print(f"\nTotal chr7 variants: {len(lines2)}")
+print("First few:")
+for line in lines2[:5]:
+    print(line)
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Number of variants in CFTR region: 354
+7	117122535	.	A	G	.	.	AC=1;AN=2;ANN=G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+2334A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+6673A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+2787A>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+2334A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+2334A>G||||||WARNING_TRANSCRIPT_INCOMPLETE	GT	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1|0	.	.
+7	117124163	.	A	C	.	.	AC=1;AN=2;ANN=C|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-3628A>C|||||3628|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+3962A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+8301A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+4415A>C||||||WARNING_TRANSCRIPT_INCOMPLETE,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+3962A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+3962A>C||||||WARNING_TRANSCRIPT_INCOMPLETE	GT	.	.	.	.	1/0	.	.	.	.	.	.	.	.	.	.	.	.
+7	117124798	.	T	G	.	.	AC=1;AN=2;ANN=G|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-2993T>G|||||2993|,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+4597T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+8936T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+5050T>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+4597T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+4597T>G||||||WARNING_TRANSCRIPT_INCOMPLETE	GT	1|0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
+7	117125573	.	A	G	.	.	AC=1;AN=2;ANN=G|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-2218A>G|||||2218|,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+5372A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+9711A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+5825A>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+5372A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+5372A>G||||||WARNING_TRANSCRIPT_INCOMPLETE	GT	.	.	.	.	1/0	.	.	.	.	.	.	.	.	.	.	.	.
+7	117126401	.	G	A	.	.	AC=8;AN=16;ANN=A|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-1390G>A|||||1390|,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+6200G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+10539G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+6653G>A||||||WARNING_TRANSCRIPT_INCOMPLETE,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+6200G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+6200G>A||||||WARNING_TRANSCRIPT_INCOMPLETE	GT	1/0	.	.	1/0	1/0	1/0	.	.	.	.	1/0	1/0	.	1/0	.	.	1/0
+
+Total chr7 variants: 499080
+First few:
+7 16487 G T
+7 16671 T C
+7 16692 G C
+7 16712 T G
+7 16717 CTG ATA
+</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+# Now let's parse the VCF for CFTR region more carefully using Python
+import subprocess
+import re
+
+# Extract the CFTR region variants with their genotypes
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# Headers
+result = subprocess.run(["grep", "^#CHROM", vcf_path], capture_output=True, text=True)
+headers = result.stdout.strip().split('\t')
+print("Samples order:", headers[9:])
+
+# Extract all CFTR region variants
+result = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} $1=="7" && $2>=117120016 && $2<=117308718 {print}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines = result.stdout.strip().split('\n')
+print(f"Total CFTR region variants: {len(lines)}")
+
+# Parse each line
+import pandas as pd
+
+records = []
+for line in lines:
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    chrom = fields[0]
+    pos = fields[1]
+    vid = fields[2]
+    ref = fields[3]
+    alt = fields[4]
+    qual = fields[5]
+    filt = fields[6]
+    info = fields[7]
+    fmt = fields[8]
+    gts = fields[9:]
+    
+    # Parse ANN field
+    ann_match = re.search(r'ANN=([^\t]+)', info)
+    ann = ann_match.group(1) if ann_match else ''
+    
+    record = {
+        'chromosome': chrom,
+        'position': int(pos),
+        'variant_id': vid,
+        'reference': ref,
+        'alternate': alt,
+        'ANN': ann,
+    }
+    
+    # Parse genotypes for each sample
+    samples = headers[9:]
+    for i, s in enumerate(samples):
+        record[s] = gts[i]
+    
+    records.append(record)
+
+df = pd.DataFrame(records)
+print(f"\nDataFrame shape: {df.shape}")
+print("\nFirst few rows:")
+print(df[['chromosome', 'position', 'reference', 'alternate', 'ANN']].head(10).to_string())
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>The output is too long to be added to context. Here are the first 10K characters...
+Samples order: ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', 'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', 'NA12891', 'NA12892', 'NA12893']
+Total CFTR region variants: 354
+
+DataFrame shape: (354, 23)
+
+First few rows:
+  chromosome   position reference alternate                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            ANN
+0          7  117122535         A         G                                                                                                                                                                                                                                                                                                                                          G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+2334A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+6673A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+2787A>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+2334A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+2334A>G||||||WARNING_TRANSCRIPT_INCOMPLETE
+1          7  117124163         A         C                                                                                                                                                                                                        C|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-3628A>C|||||3628|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+3962A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+8301A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+4415A>C||||||WARNING_TRANSCRIPT_INCOMPLETE,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+3962A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+3962A>C||||||WARNING_TRANSCRIPT_INCOMPLETE
+2          7  117124798         T         G                                                                                                                                                                                                        G|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-2993T>G|||||2993|,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+4597T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+8936T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+5050T>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+4597T>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+4597T>G||||||WARNING_TRANSCRIPT_INCOMPLETE
+3          7  117125573         A         G                                                                                                                                                                                                        G|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-2218A>G|||||2218|,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+5372A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+9711A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+5825A>G||||||WARNING_TRANSCRIPT_INCOMPLETE,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+5372A>G||||||,G|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+5372A>G||||||WARNING_TRANSCRIPT_INCOMPLETE
+4          7  117126401         G         A                                                                                                                                                                                                       A|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-1390G>A|||||1390|,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+6200G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+10539G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+6653G>A||||||WARNING_TRANSCRIPT_INCOMPLETE,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+6200G>A||||||,A|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+6200G>A||||||WARNING_TRANSCRIPT_INCOMPLETE
+5          7  117126950         G     GGAGA  GGAGA|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-841_-840insGAGA|||||840|,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+6768_53+6771dupGAGA||||||INFO_REALIGN_3_PRIME,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+11107_166+11110dupGAGA||||||INFO_REALIGN_3_PRIME,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+7221_-191+7224dupGAGA||||||WARNING_TRANSCRIPT_INCOMPLETE&INFO_REALIGN_3_PRIME,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+6768_53+6771dupGAGA||||||INFO_REALIGN_3_PRIME,GGAGA|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+6768_53+6771dupGAGA||||||WARNING_TRANSCRIPT_INCOMPLETE&INFO_REALIGN_3_PRIME
+6          7  117127739         A         C                                                                                                                                                                                                           C|upstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.-52A>C|||||52|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+7538A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+11877A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+7991A>C||||||WARNING_TRANSCRIPT_INCOMPLETE,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+7538A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+7538A>C||||||WARNING_TRANSCRIPT_INCOMPLETE
+7          7  117128942         A         C                                                                                                                                                                                                       C|downstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.*959A>C|||||959|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+8741A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+13080A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding|2/4|c.-191+9194A>C||||||WARNING_TRANSCRIPT_INCOMPLETE,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|1/25|c.53+8741A>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|1/25|c.53+8741A>C||||||WARNING_TRANSCRIPT_INCOMPLETE
+8          7  117129404         T         C                                                                                                                                                                                                     C|downstream_gene_variant|MODIFIER|AC000111.4|ENSG00000237974|transcript|ENST00000433478|processed_pseudogene||n.*1421T>C|||||1421|,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|1/26|c.53+9203T>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000546407|processed_transcript|2/2|n.166+13542T>C||||||,C|intron_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000446805|protein_coding</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+import subprocess
+import re
+import pandas as pd
+
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# Extract CFTR region variants with genotypes
+result = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} $1=="7" && $2>=117120016 && $2<=117308718 {print}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines = result.stdout.strip().split('\n')
+print(f"Total CFTR region variants: {len(lines)}")
+
+# Parse genotypes specifically
+affected = ['NA12879', 'NA12885', 'NA12886']
+parents = ['NA12877', 'NA12878']
+unaffected_siblings = ['NA12880', 'NA12881', 'NA12882', 'NA12883', 'NA12884', 'NA12887', 'NA12888', 'NA12893']
+grandparents = ['NA12889', 'NA12890', 'NA12891', 'NA12892']
+
+# Samples order from VCF
+samples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', 
+           'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', 
+           'NA12891', 'NA12892', 'NA12893']
+
+def parse_gt(gt_field):
+    """Parse genotype field, handling . and | formats"""
+    gt_field = gt_field.strip()
+    if gt_field == '.' or gt_field == '':
+        return None
+    
+    # Handle phased (0|1) and unphased (0/1) formats
+    for sep in ['|', '/']:
+        if sep in gt_field:
+            parts = gt_field.split(sep)
+            try:
+                a1 = int(parts[0]) if parts[0] != '.' else None
+                a2 = int(parts[1]) if parts[1] != '.' else None
+                return (a1, a2)
+            except (ValueError, IndexError):
+                return None
+    
+    # Handle the case where it's just a number with phasing
+    try:
+        return (int(gt_field), int(gt_field))
+    except ValueError:
+        return None
+
+candidates = []
+for line in lines:
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    chrom = fields[0]
+    pos = fields[1]
+    vid = fields[2]
+    ref = fields[3]
+    alt = fields[4]
+    info = fields[7]
+    gts = fields[9:]
+    
+    # Parse genotypes
+    sample_gts = {}
+    for i, s in enumerate(samples):
+        sample_gts[s] = parse_gt(gts[i])
+    
+    # Check if all affected are homozygous alt (1/1)
+    affected_ok = True
+    for s in affected:
+        gt = sample_gts[s]
+        if gt is None or gt[0] != 1 or gt[1] != 1:
+            affected_ok = False
+            break
+    
+    if not affected_ok:
+        continue
+    
+    # Check parents are heterozygous (0/1)
+    parents_ok = True
+    for s in parents:
+        gt = sample_gts[s]
+        if gt is None or (gt[0], gt[1]) != (0, 1):
+            parents_ok = False
+            break
+    
+    if not parents_ok:
+        continue
+    
+    # Check unaffected siblings are NOT homozygous alt
+    unaffected_ok = True
+    for s in unaffected_siblings:
+        gt = sample_gts[s]
+        if gt is not None and gt[0] == 1 and gt[1] == 1:
+            unaffected_ok = False
+            break
+    
+    if not unaffected_ok:
+        continue
+    
+    # This is a candidate
+    # Extract ANN info
+    ann_match = re.search(r'ANN=([^\t]+)', info)
+    ann = ann_match.group(1) if ann_match else ''
+    
+    print(f"\n=== CANDIDATE at chr{chrom}:{pos} {ref}>{alt} ===")
+    print(f"  ANN: {ann[:200]}")
+    for s in samples:
+        gt = sample_gts[s]
+        gt_str = f"{gt[0]}|{gt[1]}" if gt else "./."
+        marker = ""
+        if s in affected:
+            marker = " [AFFECTED]"
+        elif s in parents:
+            marker = " [PARENT]"
+        elif s in unaffected_siblings:
+            marker = " [UNAFFECTED]"
+        elif s in grandparents:
+            marker = " [GRANDPARENT]"
+        print(f"  {s}: {gt_str}{marker}")
+    
+    candidates.append({
+        'chromosome': chrom,
+        'position': int(pos),
+        'variant_id': vid if vid != '.' else '',
+        'reference': ref,
+        'alternate': alt,
+        'ANN': ann,
+    })
+
+print(f"\n\nTotal candidates with recessive pattern: {len(candidates)}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Total CFTR region variants: 354
+
+
+Total candidates with recessive pattern: 0
+</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+# Step 3 continued: Let's check the genotypes more carefully
+# Maybe the affected samples have ./. (missing) or different patterns
+# Let's look at the first few CFTR variants more closely
+
+import subprocess
+
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# Extract CFTR region variants
+result = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} $1=="7" && $2>=117120016 && $2<=117308718 {print $1,$2,$4,$5,$9,$10,$11,$12,$13,$14,$15,$16,$17,$18,$19,$20,$21,$22,$23,$24,$25,$26}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines = result.stdout.strip().split('\n')
+print(f"Total CFTR variants: {len(lines)}")
+
+# Check the genotypes for affected individuals more carefully
+samples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', 
+           'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', 
+           'NA12891', 'NA12892', 'NA12893']
+
+# Let me check what GT formats are used and look at all variants where affected share something
+all_records = []
+for line in lines:
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    chrom = fields[0]
+    pos = fields[1]
+    ref = fields[2]
+    alt = fields[3]
+    fmt = fields[4]  # FORMAT field
+    gts = fields[5:]  # Genotype fields
+    
+    # Just check the first few characters of each GT to understand format
+    affected_gts = gts[2:5]  # NA12879, NA12880, NA12881 correspond to indices 2,3,4
+    all_records.append((int(pos), ref, alt, affected_gts, gts))
+
+# Let's find all variants where affected have 0/1 or 1/1 patterns
+print("\nScanning for variants with interesting patterns in affected...")
+for pos, ref, alt, affected_gts, all_gts in all_records:
+    # Check if all three affected have the same alt allele (either het or hom)
+    gt_set = set()
+    for gt in affected_gts:
+        gt_set.add(gt[:3])  # First 3 chars should be like "0/1", "1/1", "0/0", etc.
+    
+    if len(gt_set) == 1 and list(gt_set)[0] != '0|0' and list(gt_set)[0] != '0/0' and list(gt_set)[0] != '1|.' and list(gt_set)[0] != '.|.' and list(gt_set)[0] != './.':
+        print(f"Pos {pos}: {ref}>{alt}, Affected GTs: {affected_gts}")
+
+# Also check wider region around CFTR
+print("\n\nChecking all chr7 variants near CFTR with interesting patterns...")
+result2 = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} $1=="7" && $2>=117000000 && $2<=117400000 {print $1,$2,$4,$5,$10,$11,$12}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines2 = result2.stdout.strip().split('\n')
+print(f"Total chr7:117000000-117400000 variants: {len(lines2)}")
+for line in lines2[:20]:
+    fields = line.split('\t')
+    if len(fields) >= 7:
+        print(f"  pos={fields[1]}, ref={fields[2]}, alt={fields[3]}, NA12879={fields[4]}, NA12885={fields[5]}, NA12886={fields[6]}")
+</issue>
+
+<execute>
+# Let me check the GT format and what values we actually have.
+# The samples order is: NA12877 NA12878 NA12879 NA12880 NA12881 NA12882 NA12883 NA12884 NA12885 NA12886 NA12887 NA12888 NA12889 NA12890 NA12891 NA12892 NA12893
+# Affected: NA12879 (idx 2), NA12885 (idx 8), NA12886 (idx 9)
+
+import subprocess
+
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# Let's get the header to know which column is which
+result = subprocess.run(["grep", "^#CHROM", vcf_path], capture_output=True, text=True)
+header_fields = result.stdout.strip().split('\t')
+print("Header samples (indices):")
+for i, s in enumerate(header_fields[9:]):
+    print(f"  {i}: {s}")
+
+# Extract all CFTR exon/intron variants and look at a few
+result = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} NR>1 && $1=="7" && $2>=117120016 && $2<=117308718 {print}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines = result.stdout.strip().split('\n')
+print(f"\nTotal CFTR variants: {len(lines)}")
+
+# Check a sample of variants for patterns
+print("\n=== Checking GT patterns across entire VCF for affected siblings ===")
+result3 = subprocess.run([
+    "awk",
+    '-F\t',
+    'BEGIN{OFS="\t"} NR>1 {print $1,$2,$4,$5,$10,$11,$12}',
+    vcf_path
+], capture_output=True, text=True)
+
+# Read into Python
+grep_lines = result3.stdout.strip().split('\n')
+print(f"Total variant lines: {len(grep_lines)}")
+
+# Let's look for variants where affected have 1/1 pattern
+count = 0
+for line in grep_lines:
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    if len(fields) >= 7:
+        na12879_gt = fields[4].strip()
+        na12878_gt = fields[5].strip()
+        na12886_gt = fields[6].strip()
+        
+        is_hom_alt = all(gt in ['1/1', '1|1'] for gt in [na12879_gt, na12878_gt, na12886_gt] if gt not in ['.', './.', '.|.'])
+        
+        if is_hom_alt:
+            print(f"  chr{fields[0]}:{fields[1]} {fields[2]}>{fields[3]} - GT79={na12879_gt}, GT85={na12878_gt}, GT86={na12886_gt}")
+            count += 1
+
+print(f"\nTotal variants with all affected 1/1: {count}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Error: invalid syntax (<string>, line 68)</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+import subprocess
+
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# Get the header to know which column is which
+result = subprocess.run(["grep", "^#CHROM", vcf_path], capture_output=True, text=True)
+header_fields = result.stdout.strip().split('\t')
+print("Header samples (indices):")
+for i, s in enumerate(header_fields[9:]):
+    print(f"  {i}: {s}")
+
+# Extract all CFTR variants
+result = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} NR>1 && $1=="7" && $2>=117120016 && $2<=117308718 {print}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines = result.stdout.strip().split('\n')
+print(f"\nTotal CFTR variants: {len(lines)}")
+
+# Let me check a few samples to understand the GT format
+print("\n=== First 10 CFTR variants - GT columns ===")
+for i, line in enumerate(lines[:10]):
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    # columns: CHROM POS ID REF ALT QUAL FILTER INFO FORMAT then 17 sample GTs
+    chrom = fields[0]
+    pos = fields[1]
+    ref = fields[3]
+    alt = fields[4]
+    # Sample GTs: indices 9-25
+    na12879_gt = fields[11]  # index 2 in samples
+    na12885_gt = fields[17]  # index 8 in samples  
+    na12886_gt = fields[18]  # index 9 in samples
+    na12877_gt = fields[9]   # index 0 (father)
+    na12878_gt = fields[10]  # index 1 (mother)
+    print(f"  chr{chrom}:{pos} {ref}>{alt} | GT77={na12877_gt} GT78={na12878_gt} GT79={na12879_gt} GT85={na12885_gt} GT86={na12886_gt}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Header samples (indices):
+  0: NA12877
+  1: NA12878
+  2: NA12879
+  3: NA12880
+  4: NA12881
+  5: NA12882
+  6: NA12883
+  7: NA12884
+  8: NA12885
+  9: NA12886
+  10: NA12887
+  11: NA12888
+  12: NA12889
+  13: NA12890
+  14: NA12891
+  15: NA12892
+  16: NA12893
+
+Total CFTR variants: 354
+
+=== First 10 CFTR variants - GT columns ===
+  chr7:117122535 A>G | GT77=. GT78=. GT79=. GT85=. GT86=.
+  chr7:117124163 A>C | GT77=. GT78=. GT79=. GT85=. GT86=.
+  chr7:117124798 T>G | GT77=1|0 GT78=. GT79=. GT85=. GT86=.
+  chr7:117125573 A>G | GT77=. GT78=. GT79=. GT85=. GT86=.
+  chr7:117126401 G>A | GT77=1/0 GT78=. GT79=. GT85=. GT86=.
+  chr7:117126950 G>GGAGA | GT77=1/0 GT78=. GT79=. GT85=. GT86=.
+  chr7:117127739 A>C | GT77=1/0 GT78=. GT79=. GT85=. GT86=.
+  chr7:117128942 A>C | GT77=1/1 GT78=. GT79=1|0 GT85=1|0 GT86=1|0
+  chr7:117129404 T>C | GT77=1/1 GT78=. GT79=1|0 GT85=1|0 GT86=1|0
+  chr7:117129491 A>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+</observation>
+
+================================== Ai Message ==================================
+
+<execute>
+import subprocess
+
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# Extract all CFTR variants and check GT patterns
+result = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} NR>1 && $1=="7" && $2>=117120016 && $2<=117308718 {print}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines = result.stdout.strip().split('\n')
+print(f"Total CFTR variants: {len(lines)}")
+
+# Scan for variants where affected are 1/1 or 1|1
+affected_idx = [2, 8, 9]  # NA12879, NA12885, NA12886
+mother_idx = 1  # NA12878
+father_idx = 0  # NA12877
+
+print("\n=== Variants where affected are 1/1 or 1|1 ===")
+count_affected_hom = 0
+for line in lines:
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    chrom = fields[0]
+    pos = fields[1]
+    ref = fields[3]
+    alt = fields[4]
+    
+    # Get genotypes for affected
+    gt_79 = fields[11]  # affected_idx[0] + 9
+    gt_85 = fields[17]  # affected_idx[1] + 9
+    gt_86 = fields[18]  # affected_idx[2] + 9
+    
+    gts_affected = [gt_79, gt_85, gt_86]
+    
+    # Check if any affected is 1/1 or 1|1
+    for gt in gts_affected:
+        if gt in ['1/1', '1|1']:
+            gt_father = fields[9]
+            gt_mother = fields[10]
+            print(f"  chr{chrom}:{pos} {ref}>{alt} | GT77={gt_father} GT78={gt_mother} GT79={gt_79} GT85={gt_85} GT86={gt_86}")
+            count_affected_hom += 1
+            break
+
+print(f"\nTotal with at least one affected 1/1: {count_affected_hom}")
+
+# Now also check all non-missing variants among affected
+print("\n=== All CFTR variants with non-missing GT for all three affected ===")
+count_non_missing = 0
+for line in lines:
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    pos = fields[1]
+    ref = fields[3]
+    alt = fields[4]
+    
+    gt_79 = fields[11]
+    gt_85 = fields[17]
+    gt_86 = fields[18]
+    
+    if gt_79 not in ['.', './.', '.|.'] and gt_85 not in ['.', './.', '.|.'] and gt_86 not in ['.', './.', '.|.']:
+        gt_father = fields[9]
+        gt_mother = fields[10]
+        print(f"  chr7:{pos} {ref}>{alt} | GT77={gt_father} GT78={gt_mother} GT79={gt_79} GT85={gt_85} GT86={gt_86}")
+        count_non_missing += 1
+        if count_non_missing >= 30:
+            print("  ... (stopping after 30)")
+            break
+
+print(f"\nTotal with non-missing GT for all affected: {count_non_missing}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Total CFTR variants: 354
+
+=== Variants where affected are 1/1 or 1|1 ===
+  chr7:117149761 T>C | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117223442 TGG>CGC | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117224440 G>A | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117227832 G>T | GT77=1/0 GT78=1/0 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117236235 G>GC | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117238476 G>A | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117256289 C>CT | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117263417 G>C | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117287477 T>C | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117300742 G>A | GT77=1|0 GT78=. GT79=1|1 GT85=. GT86=.
+
+Total with at least one affected 1/1: 10
+
+=== All CFTR variants with non-missing GT for all three affected ===
+  chr7:117128942 A>C | GT77=1/1 GT78=. GT79=1|0 GT85=1|0 GT86=1|0
+  chr7:117129404 T>C | GT77=1/1 GT78=. GT79=1|0 GT85=1|0 GT86=1|0
+  chr7:117129491 A>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117129562 T>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117132919 A>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117134492 TC>T | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117141835 AA>CA | GT77=1|0 GT78=. GT79=1|. GT85=1|0 GT86=1|.
+  chr7:117147500 C>G | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117149761 T>C | GT77=1/1 GT78=1/1 GT79=1/1 GT85=1/1 GT86=1/1
+  chr7:117151828 A>C | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117156341 T>C | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117161653 T>G | GT77=1/1 GT78=. GT79=1|0 GT85=1/0 GT86=1/0
+  chr7:117162140 T>C | GT77=. GT78=1/0 GT79=0|1 GT85=1/0 GT86=1/0
+  chr7:117163047 T>A | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117164439 T>C | GT77=1|0 GT78=. GT79=1/0 GT85=1|0 GT86=1|0
+  chr7:117172221 G>A | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117172269 T>A | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117172731 A>G | GT77=1|0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117173230 T>C | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117175791 T>A | GT77=1|0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117178208 A>G | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117178754 A>G | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117178807 TA>T | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117181704 AT>A | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117184516 G>A | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117185071 T>C | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117190066 C>T | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117190703 C>T | GT77=1|0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117191283 G>A | GT77=1/1 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  chr7:117194470 G>A | GT77=1/0 GT78=. GT79=1/0 GT85=1/0 GT86=1/0
+  ... (stopping after 30)
+
+Total with non-missing GT for all affected: 30
+</observation>
+
+================================== Ai Message ==================================
+
+Good findings! I can see position 7:117227832 stands out with recessive pattern. Let me investigate more systematically.
+
+<execute>
+import subprocess
+import re
+
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+# Extract CFTR region variants
+result = subprocess.run([
+    "awk", 
+    '-F\t', 
+    'BEGIN{OFS="\t"} NR>1 && $1=="7" && $2>=117120016 && $2<=117308718 {print}',
+    vcf_path
+], capture_output=True, text=True)
+
+lines = result.stdout.strip().split('\n')
+print(f"Total CFTR variants: {len(lines)}")
+
+# Find variants with proper recessive pattern: parents 0/1, affected 1/1
+print("\n=== Variants with proper recessive pattern (parents 0/1, affected 1/1) ===")
+for line in lines:
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    chrom = fields[0]
+    pos = int(fields[1])
+    vid = fields[2]
+    ref = fields[3]
+    alt = fields[4]
+    info = fields[7]
+    
+    gt_father = fields[9]   # NA12877
+    gt_mother = fields[10]  # NA12878
+    gt_79 = fields[11]      # NA12879
+    gt_85 = fields[17]      # NA12885
+    gt_86 = fields[18]      # NA12886
+    
+    # Check recessive: parents both 0/1, all three affected 1/1
+    parents_het = gt_father in ['0/1', '0|1'] and gt_mother in ['0/1', '0|1']
+    affected_hom = all(gt in ['1/1', '1|1'] for gt in [gt_79, gt_85, gt_86])
+    
+    if parents_het and affected_hom:
+        # Get ANN info
+        ann_match = re.search(r'ANN=([^\t]+)', info)
+        ann = ann_match.group(1) if ann_match else ''
+        
+        # Also check unaffected siblings
+        unaffected_gts = []
+        for idx in [3, 4, 5, 6, 7, 10, 11, 16]:  # indices for unaffected siblings
+            gt = fields[idx + 9]
+            unaffected_gts.append(gt)
+        
+        print(f"\n=== RECESSIVE CANDIDATE at chr{chrom}:{pos} {ref}>{alt} ===")
+        print(f"  ID: {vid}")
+        print(f"  Father: {gt_father}, Mother: {gt_mother}")
+        print(f"  Affected: 79={gt_79}, 85={gt_85}, 86={gt_86}")
+        print(f"  Unaffected siblings: {unaffected_gts}")
+        print(f"  ANN: {ann}")
+        
+# Also check: what about chr7:117227832 more carefully?
+print("\n\n=== Details for chr7:117227832 ===")
+for line in lines:
+    if not line.strip():
+        continue
+    fields = line.split('\t')
+    if fields[1] == '117227832':
+        info = fields[7]
+        ann_match = re.search(r'ANN=([^\t]+)', info)
+        ann = ann_match.group(1) if ann_match else ''
+        print(f"Full record:")
+        print(f"  POS: {fields[1]}")
+        print(f"  REF: {fields[3]}, ALT: {fields[4]}")
+        print(f"  INFO field: {info[:500]}")
+        print(f"  ANN: {ann}")
+        
+        # Print all genotypes
+        samples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', 
+                   'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', 
+                   'NA12891', 'NA12892', 'NA12893']
+        for i, s in enumerate(samples):
+            print(f"  {s}: {fields[i+9]}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Total CFTR variants: 354
+
+=== Variants with proper recessive pattern (parents 0/1, affected 1/1) ===
+
+
+=== Details for chr7:117227832 ===
+Full record:
+  POS: 117227832
+  REF: G, ALT: T
+  INFO field: AC=14;AN=22;ANN=T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|12/27|c.1624G>T|p.Gly542*|1756/6128|1624/4443|542/1480||,T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|11/26|c.1441G>T|p.Gly481*|1573/5949|1441/4260|481/1419||,T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|11/26|c.1534G>T|p.Gly512*|1534/4316|1534/4316|512/1437||WARNING_TRANSCRIPT_INCOMPLETE,T|upstream_gene_variant|MODIFIER|AC000
+  ANN: T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000003084|protein_coding|12/27|c.1624G>T|p.Gly542*|1756/6128|1624/4443|542/1480||,T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000454343|protein_coding|11/26|c.1441G>T|p.Gly481*|1573/5949|1441/4260|481/1419||,T|stop_gained|HIGH|CFTR|ENSG00000001626|transcript|ENST00000426809|protein_coding|11/26|c.1534G>T|p.Gly512*|1534/4316|1534/4316|512/1437||WARNING_TRANSCRIPT_INCOMPLETE,T|upstream_gene_variant|MODIFIER|AC000111.5|ENSG00000234001|transcript|ENST00000448200|processed_pseudogene||n.-1362C>A|||||1362|,T|downstream_gene_variant|MODIFIER|CFTR|ENSG00000001626|transcript|ENST00000472848|processed_transcript||n.*29G>T|||||29|;LOF=(CFTR|ENSG00000001626|11|0.27);NMD=(CFTR|ENSG00000001626|11|0.27)
+  NA12877: 1/0
+  NA12878: 1/0
+  NA12879: 1/1
+  NA12880: .
+  NA12881: .
+  NA12882: 1/0
+  NA12883: 1/0
+  NA12884: 1/0
+  NA12885: 1/1
+  NA12886: 1/1
+  NA12887: .
+  NA12888: 1/0
+  NA12889: 1/0
+  NA12890: .
+  NA12891: .
+  NA12892: 1/0
+  NA12893: .
+</observation>
+
+================================== Ai Message ==================================
+
+Found it! The variant at chr7:117227832 (G>T, causing p.Gly542* stop-gained in CFTR) shows perfect recessive inheritance. Now let me cross-reference with ClinVar.
+
+<execute>
+# Step 4: Cross-reference with ClinVar VCF
+import subprocess
+import re
+
+clinvar_vcf = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference/clinvar_20250521.vcf.gz"
+
+# Check chromosome naming in ClinVar - it might use "NC_000007.13" or just "7"
+# Let's look at a few variants to understand the format
+result = subprocess.run(["gunzip", "-c", clinvar_vcf, "|", "head", "-50"], capture_output=True, text=True, shell=True)
+print(result.stdout[:2000])
+
+# Check if tabix index exists
+result2 = subprocess.run(["ls", "-la", clinvar_vcf.replace(".gz", "")], capture_output=True, text=True)
+print(f"\nUnzipped file: {result2.stdout}")
+
+# Use bcftools or tabix to query the region
+result3 = subprocess.run(["tabix", clinvar_vcf, "7:117227832-117227832"], capture_output=True, text=True)
+print(f"\nTabix query result:")
+print(result3.stdout)
+print(f"Stderr: {result3.stderr}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>
+
+Unzipped file: 
+
+Tabix query result:
+7	117227790	634914	TAGGACATCTCCAAGTTTGCAGAGAAAGACAATATAGTTCTTGGAGAAGGTGGAATCACACTGAGTGGAGGTCAACGAGCAAGAATTTCTTTAGCA	T	.	.	ALLELEID=622747;CLNDISDB=MONDO:MONDO:0009061,MedGen:C0010674,OMIM:219700,Orphanet:586;CLNDN=Cystic_fibrosis;CLNHGVS=NC_000007.13:g.117227794_117227888del;CLNREVSTAT=criteria_provided,_single_submitter;CLNSIG=Pathogenic;CLNSIGSCV=SCV000924336;CLNVC=Deletion;CLNVCSO=SO:0000159;CLNVI=ClinGen:CA913190192;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001574|splice_acceptor_variant,SO:0001575|splice_donor_variant;ORIGIN=0;RS=1562906265
+7	117227831	1434498	TG	T	.	.	ALLELEID=1494314;CLNDISDB=MONDO:MONDO:0009061,MedGen:C0010674,OMIM:219700,Orphanet:586;CLNDN=Cystic_fibrosis;CLNHGVS=NC_000007.13:g.117227833del;CLNREVSTAT=criteria_provided,_single_submitter;CLNSIG=Pathogenic;CLNSIGSCV=SCV002209119;CLNVC=Deletion;CLNVCSO=SO:0000159;CLNVI=ClinGen:CA2573141659;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001589|frameshift_variant;ORIGIN=1;RS=2116017718
+7	117227832	3233783	G	A	.	.	ALLELEID=3392906;CLNDISDB=MedGen:CN169374;CLNDN=not_specified;CLNHGVS=NC_000007.13:g.117227832G>A;CLNREVSTAT=criteria_provided,_single_submitter;CLNSIG=Uncertain_significance;CLNSIGSCV=SCV005039160;CLNVC=single_nucleotide_variant;CLNVCSO=SO:0001483;CLNVI=ClinGen:CA368975971;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001583|missense_variant;ORIGIN=1;RS=113993959
+7	117227832	7115	G	T	.	.	AF_EXAC=0.00026;ALLELEID=22154;CLNDISDB=MONDO:MONDO:0009061,MedGen:C0010674,OMIM:219700,Orphanet:586|MONDO:MONDO:0010178,MedGen:C0403814,OMIM:277180,Orphanet:48|MONDO:MONDO:0008185,MedGen:C0238339,OMIM:167800,Orphanet:676|MONDO:MONDO:0008887,MedGen:C2749757,OMIM:211400,Orphanet:60033|MedGen:C5924204|MedGen:C3661900;CLNDN=Cystic_fibrosis|Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation|Hereditary_pancreatitis|Bronchiectasis_with_or_without_elevated_sweat_chloride_1|CFTR-related_disorder|not_provided;CLNHGVS=NC_000007.13:g.117227832G>T;CLNREVSTAT=practice_guideline;CLNSIG=Pathogenic;CLNSIGSCV=SCV000071394;CLNVC=single_nucleotide_variant;CLNVCSO=SO:0001483;CLNVI=ClinGen:CA284836|Genetic_Testing_Registry_(GTR):GTR000028916|Genetic_Testing_Registry_(GTR):GTR000074114|Genetic_Testing_Registry_(GTR):GTR000500233|Genetic_Testing_Registry_(GTR):GTR000576392|OMIM:602421.0009|OMIM:602421.0095;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001587|nonsense;ORIGIN=13;RS=113993959
+
+Stderr: 
+</observation>
+
+================================== Ai Message ==================================
+
+Excellent! I found the causal variant. Let me now build the complete output CSV.
+
+<execute>
+import subprocess
+import re
+
+# The causal variant: chr7:117227832 G>T, CFTR c.1624G>T p.Gly542* (G542X)
+
+# Verify all genotypes one more time to be thorough
+vcf_path = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data/ex1.eff.vcf"
+
+result = subprocess.run([
+    "awk",
+    '-F\t',
+    'BEGIN{OFS="\t"} NR>1 && $1=="7" && $2>=117227832 && $2<=117227832 {print}',
+    vcf_path
+], capture_output=True, text=True)
+
+line = result.stdout.strip()
+fields = line.split('\t')
+
+samples = ['NA12877', 'NA12878', 'NA12879', 'NA12880', 'NA12881', 'NA12882', 'NA12883', 
+           'NA12884', 'NA12885', 'NA12886', 'NA12887', 'NA12888', 'NA12889', 'NA12890', 
+           'NA12891', 'NA12892', 'NA12893']
+
+print("Genotype summary for chr7:117227832 G>T (CFTR c.1624G>T p.Gly542*):")
+for i, s in enumerate(samples):
+    gt = fields[i+9]
+    role = ""
+    if s in ['NA12877']: role = "(Father)"
+    elif s in ['NA12878']: role = "(Mother)"
+    elif s in ['NA12879', 'NA12885', 'NA12886']: role = "(AFFECTED)"
+    elif s in ['NA12889', 'NA12890', 'NA12891', 'NA12892']: role = "(Grandparent)"
+    else: role = "(Sibling)"
+    
+    interpretation = ""
+    if gt in ['1/1', '1|1']:
+        interpretation = "Homozygous alt - affected"
+    elif gt in ['0/1', '1/0', '0|1', '1|0']:
+        interpretation = "Heterozygous carrier"
+    elif gt in ['0/0', '0|0']:
+        interpretation = "Homozygous ref - unaffected"
+    else:
+        interpretation = f"No call ({gt})"
+    
+    print(f"  {s:12s} {role:15s} GT={gt:5s}  {interpretation}")
+
+print("\n\nThis confirms recessive inheritance pattern:")
+print("  - Both parents are carriers (0/1)")
+print("  - All three affected siblings are homozygous for the mutation (1/1)")
+print("  - Unaffected siblings are either carriers (0/1) or have no call")
+
+# Now cross-reference with ClinVar for the exact variant
+clinvar_vcf = "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference/clinvar_20250521.vcf.gz"
+result2 = subprocess.run(["tabix", clinvar_vcf, "7:117227832-117227832"], capture_output=True, text=True)
+
+# Parse the ClinVar record for the G>T variant specifically
+for cline in result2.stdout.strip().split('\n'):
+    if not cline:
+        continue
+    clfields = cline.split('\t')
+    ref = clfields[3]
+    alt = clfields[4]
+    info = clfields[7]
+    
+    if ref == 'G' and alt == 'T':
+        print(f"\nClinVar record for G>T:")
+        print(f"  Position: {clfields[1]}")
+        print(f"  RS ID: {clfields[2]}")
+        print(f"  INFO: {info}")
+        
+        # Parse key ClinVar fields
+        info_parts = info.split(';')
+        clin_parsed = {}
+        for part in info_parts:
+            if '=' in part:
+                key, val = part.split('=', 1)
+                clin_parsed[key] = val
+        
+        print(f"\n  CLNSIG: {clin_parsed.get('CLNSIG', 'N/A')}")
+        print(f"  CLNDN: {clin_parsed.get('CLNDN', 'N/A')}")
+        print(f"  CLNREVSTAT: {clin_parsed.get('CLNREVSTAT', 'N/A')}")
+        print(f"  RS: {clfields[2]}")
+        print(f"  GENE: {clin_parsed.get('GENEINFO', 'N/A')}")
+        print(f"  CLNHGVS: {clin_parsed.get('CLNHGVS', 'N/A')}")
+        print(f"  MC: {clin_parsed.get('MC', 'N/A')}")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>Genotype summary for chr7:117227832 G>T (CFTR c.1624G>T p.Gly542*):
+  NA12877      (Father)        GT=1/0    Heterozygous carrier
+  NA12878      (Mother)        GT=1/0    Heterozygous carrier
+  NA12879      (AFFECTED)      GT=1/1    Homozygous alt - affected
+  NA12880      (Sibling)       GT=.      No call (.)
+  NA12881      (Sibling)       GT=.      No call (.)
+  NA12882      (Sibling)       GT=1/0    Heterozygous carrier
+  NA12883      (Sibling)       GT=1/0    Heterozygous carrier
+  NA12884      (Sibling)       GT=1/0    Heterozygous carrier
+  NA12885      (AFFECTED)      GT=1/1    Homozygous alt - affected
+  NA12886      (AFFECTED)      GT=1/1    Homozygous alt - affected
+  NA12887      (Sibling)       GT=.      No call (.)
+  NA12888      (Sibling)       GT=1/0    Heterozygous carrier
+  NA12889      (Grandparent)   GT=1/0    Heterozygous carrier
+  NA12890      (Grandparent)   GT=.      No call (.)
+  NA12891      (Grandparent)   GT=.      No call (.)
+  NA12892      (Grandparent)   GT=1/0    Heterozygous carrier
+  NA12893      (Sibling)       GT=.      No call (.)
+
+
+This confirms recessive inheritance pattern:
+  - Both parents are carriers (0/1)
+  - All three affected siblings are homozygous for the mutation (1/1)
+  - Unaffected siblings are either carriers (0/1) or have no call
+
+ClinVar record for G>T:
+  Position: 117227832
+  RS ID: 7115
+  INFO: AF_EXAC=0.00026;ALLELEID=22154;CLNDISDB=MONDO:MONDO:0009061,MedGen:C0010674,OMIM:219700,Orphanet:586|MONDO:MONDO:0010178,MedGen:C0403814,OMIM:277180,Orphanet:48|MONDO:MONDO:0008185,MedGen:C0238339,OMIM:167800,Orphanet:676|MONDO:MONDO:0008887,MedGen:C2749757,OMIM:211400,Orphanet:60033|MedGen:C5924204|MedGen:C3661900;CLNDN=Cystic_fibrosis|Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation|Hereditary_pancreatitis|Bronchiectasis_with_or_without_elevated_sweat_chloride_1|CFTR-related_disorder|not_provided;CLNHGVS=NC_000007.13:g.117227832G>T;CLNREVSTAT=practice_guideline;CLNSIG=Pathogenic;CLNSIGSCV=SCV000071394;CLNVC=single_nucleotide_variant;CLNVCSO=SO:0001483;CLNVI=ClinGen:CA284836|Genetic_Testing_Registry_(GTR):GTR000028916|Genetic_Testing_Registry_(GTR):GTR000074114|Genetic_Testing_Registry_(GTR):GTR000500233|Genetic_Testing_Registry_(GTR):GTR000576392|OMIM:602421.0009|OMIM:602421.0095;GENEINFO=CFTR:1080|LOC111674475:111674475;MC=SO:0001587|nonsense;ORIGIN=13;RS=113993959
+
+  CLNSIG: Pathogenic
+  CLNDN: Cystic_fibrosis|Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation|Hereditary_pancreatitis|Bronchiectasis_with_or_without_elevated_sweat_chloride_1|CFTR-related_disorder|not_provided
+  CLNREVSTAT: practice_guideline
+  RS: 7115
+  GENE: CFTR:1080|LOC111674475:111674475
+  CLNHGVS: NC_000007.13:g.117227832G>T
+  MC: SO:0001587|nonsense
+</observation>
+
+================================== Ai Message ==================================
+
+Excellent! Now I have all the information needed. Let me construct the final CSV output.
+
+<execute>
+# Step 5: Build and save the final CSV
+import csv
+import os
+
+# The causal variant data
+output_path = "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv"
+
+row = {
+    'chromosome': '7',
+    'position': '117227832',
+    'variant_id': '7115',
+    'reference': 'G',
+    'alternate': 'T',
+    'gene_name': 'CFTR',
+    'gene_id': 'ENSG00000001626',
+    'annotation': 'stop_gained',
+    'impact': 'HIGH',
+    'transcript_id': 'ENST00000003084',
+    'hgvs_c': 'c.1624G>T',
+    'hgvs_p': 'p.Gly542*',
+    'clinical_significance': 'Pathogenic',
+    'diseases': 'Cystic_fibrosis; Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation; Hereditary_pancreatitis; Bronchiectasis_with_or_without_elevated_sweat_chloride_1; CFTR-related_disorder; not_provided',
+    'review_status': 'practice_guideline',
+    'rs_id': 'rs113993959'
+}
+
+# Write CSV
+fieldnames = ['chromosome', 'position', 'variant_id', 'reference', 'alternate', 
+              'gene_name', 'gene_id', 'annotation', 'impact', 'transcript_id',
+              'hgvs_c', 'hgvs_p', 'clinical_significance', 'diseases', 
+              'review_status', 'rs_id']
+
+with open(output_path, 'w', newline='') as f:
+    writer = csv.DictWriter(f, fieldnames=fieldnames)
+    writer.writeheader()
+    writer.writerow(row)
+
+print(f"CSV written to {output_path}")
+print("\nContents:")
+with open(output_path, 'r') as f:
+    print(f.read())
+
+# Also verify the file exists and is readable
+print(f"File exists: {os.path.exists(output_path)}")
+print(f"File size: {os.path.getsize(output_path)} bytes")
+</execute>
+
+================================== Ai Message ==================================
+
+<observation>CSV written to /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv
+
+Contents:
+chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id
+7,117227832,7115,G,T,CFTR,ENSG00000001626,stop_gained,HIGH,ENST00000003084,c.1624G>T,p.Gly542*,Pathogenic,Cystic_fibrosis; Congenital_bilateral_aplasia_of_vas_deferens_from_CFTR_mutation; Hereditary_pancreatitis; Bronchiectasis_with_or_without_elevated_sweat_chloride_1; CFTR-related_disorder; not_provided,practice_guideline,rs113993959
+
+File exists: True
+File size: 506 bytes
+</observation>
+
+================================== Ai Message ==================================
+
+Now let me update the plan and provide the final summary.
+
+1. [✓] Read the family description to understand the pedigree and affected individuals
+2. [✓] Examine the family VCF (ex1.eff.vcf) to understand its structure
+3. [✓] Filter for variants in affected siblings (NA12885, NA12886, NA12879) that follow recessive inheritance pattern
+4. [✓] Cross-reference with ClinVar VCF to find CFTR variants with clinical significance
+5. [✓] Identify the causal recessive variant consistent with affected siblings
+6. [✓] Format and save the output CSV
+
+## Final Summary
+
+**Task**: Identify the causal recessive variant for Cystic Fibrosis in affected siblings NA12879, NA12885, and NA12886.
+
+**Approach**:
+1. Analyzed the family pedigree (2 unaffected carrier parents, 11 siblings including 3 affected)
+2. Scanned 354 variants in the CFTR gene region (chr7:117,120,016-117,308,718) from the annotated VCF
+3. Applied recessive inheritance filter (parents both heterozygous 0/1, affected siblings homozygous alt 1/1, unaffected siblings not 1/1)
+4. Cross-referenced candidates with the ClinVar VCF
+
+**Causal Variant Identified**:
+- **Position**: chr7:117227832
+- **Variant**: G>T (rs113993959, ClinVar ID 7115)
+- **Gene**: CFTR (ENSG00000001626)
+- **Coding Change**: c.1624G>T (ENST00000003084)
+- **Protein Change**: p.Gly542* (G542X) - stop_gained/nonsense mutation
+- **Clinical Significance**: Pathogenic (practice guideline reviewed)
+- **Disease**: Cystic fibrosis (and associated conditions)
+
+**Inheritance Evidence**:
+- Both parents (NA12877, NA12878) are heterozygous carriers (0/1)
+- All three affected siblings (NA12879, NA12885, NA12886) are homozygous (1/1)
+- Unaffected siblings are either carriers (0/1) or have no-call, consistent with recessive inheritance
+
+**Output file**: `/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv`
+
+<solution>
+The answer is: The causal recessive CFTR variant is chr7:117227832 G>T (rs113993959), a stop-gained mutation (c.1624G>T, p.Gly542*) with HIGH impact, classified as Pathogenic with practice guideline review status, associated with Cystic fibrosis. The CSV has been saved to the required output path.
+</solution>

Biomni/experiments/bioagent_bench/runs/scale_100/cystic-fibrosis_20260514_192139/final_answer.txt

@@ -0,0 +1,38 @@
+Now let me update the plan and provide the final summary.
+
+1. [✓] Read the family description to understand the pedigree and affected individuals
+2. [✓] Examine the family VCF (ex1.eff.vcf) to understand its structure
+3. [✓] Filter for variants in affected siblings (NA12885, NA12886, NA12879) that follow recessive inheritance pattern
+4. [✓] Cross-reference with ClinVar VCF to find CFTR variants with clinical significance
+5. [✓] Identify the causal recessive variant consistent with affected siblings
+6. [✓] Format and save the output CSV
+
+## Final Summary
+
+**Task**: Identify the causal recessive variant for Cystic Fibrosis in affected siblings NA12879, NA12885, and NA12886.
+
+**Approach**:
+1. Analyzed the family pedigree (2 unaffected carrier parents, 11 siblings including 3 affected)
+2. Scanned 354 variants in the CFTR gene region (chr7:117,120,016-117,308,718) from the annotated VCF
+3. Applied recessive inheritance filter (parents both heterozygous 0/1, affected siblings homozygous alt 1/1, unaffected siblings not 1/1)
+4. Cross-referenced candidates with the ClinVar VCF
+
+**Causal Variant Identified**:
+- **Position**: chr7:117227832
+- **Variant**: G>T (rs113993959, ClinVar ID 7115)
+- **Gene**: CFTR (ENSG00000001626)
+- **Coding Change**: c.1624G>T (ENST00000003084)
+- **Protein Change**: p.Gly542* (G542X) - stop_gained/nonsense mutation
+- **Clinical Significance**: Pathogenic (practice guideline reviewed)
+- **Disease**: Cystic fibrosis (and associated conditions)
+
+**Inheritance Evidence**:
+- Both parents (NA12877, NA12878) are heterozygous carriers (0/1)
+- All three affected siblings (NA12879, NA12885, NA12886) are homozygous (1/1)
+- Unaffected siblings are either carriers (0/1) or have no-call, consistent with recessive inheritance
+
+**Output file**: `/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv`
+
+<solution>
+The answer is: The causal recessive CFTR variant is chr7:117227832 G>T (rs113993959), a stop-gained mutation (c.1624G>T, p.Gly542*) with HIGH impact, classified as Pathogenic with practice guideline review status, associated with Cystic fibrosis. The CSV has been saved to the required output path.
+</solution>

Biomni/experiments/bioagent_bench/runs/scale_100/cystic-fibrosis_20260514_192139/output_validation.json

@@ -0,0 +1,15 @@
+{
+  "postprocess": [
+    {
+      "file": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv",
+      "actions": []
+    }
+  ],
+  "outputs": [
+    {
+      "path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv",
+      "exists": true,
+      "size_bytes": 506
+    }
+  ]
+}

Biomni/experiments/bioagent_bench/runs/scale_100/cystic-fibrosis_20260514_192139/retrieval_plan.json

@@ -0,0 +1,520 @@
+{
+  "query": "You are running a bioagent-bench task with local files already prepared.\n\nTask ID: cystic-fibrosis\nTask name: Cystic Fibrosis Mendelian Variant Identification\nBenchmark prompt:\nFind the genetic cause of Cystic fibrosis; identify the causal recessive variant consistent with affected siblings NA12885, NA12886, and NA12879. The output should be a CSV file with the following columns: chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id. <example>chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id\nX,123456789,VAR123,A,G,GENE1,ENSG00000000001,missense_variant,MODERATE,ENST00000000001,c.123A>G,p.Lys41Arg,Likely_pathogenic,Disease_A; Disease_B; not_provided,reviewed_by_expert_panel,rs0000001</example>\nData background:\nThe sample dataset is a simulated dataset for finding the genetic cause of Cystic fibrosis. The dataset is real sequencing data from CEPH_1463 dataset provided by the Complete Genomics Diversity Panel. It consists of sequencing of a family: 4 grandparents, 2 parents and 11 siblings. A known Mandelian disease mutation has been added on three siblings, taking care to be consistent with the underlying heplotype structure. The goal is to find the mutation causing the mendalian recessive trait - Cystic Fibrosis.\n\nConstraints:\n1. Use only the benchmark inputs and references explicitly listed below.\n2. Save the required final deliverables exactly to the paths listed below.\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\n5. Return a concise final summary after writing the required files.\n\nTask-specific instruction:\nUse only the provided family variant data and ClinVar VCF. The final row should identify the causal CFTR recessive variant consistent with affected siblings.\n\nBenchmark data policy:\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/data\n- Allowed reference directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/results\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than cystic-fibrosis>\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\n- Do not download external databases or install new packages during the benchmark run.\n\nInput data directory:\n/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data\nVisible input files:\n- ex1.eff.vcf\n- family_description.txt\n\nReference data directory:\n/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference\nVisible reference files:\n- clinvar_20250521.vcf.gz\n- clinvar_20250521.vcf.gz.tbi\n\nRequired final output paths:\n- cf_variants.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv",
+  "query_context": {},
+  "mcp_config": "/225040511/project/Biomni/experiments/mcp_tool_scaling/configs/mcp_scale_100.yaml",
+  "planning_context_text": "{\"prompt\": \"You are running a bioagent-bench task with local files already prepared.\\n\\nTask ID: cystic-fibrosis\\nTask name: Cystic Fibrosis Mendelian Variant Identification\\nBenchmark prompt:\\nFind the genetic cause of Cystic fibrosis; identify the causal recessive variant consistent with affected siblings NA12885, NA12886, and NA12879. The output should be a CSV file with the following columns: chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id. <example>chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id\\nX,123456789,VAR123,A,G,GENE1,ENSG00000000001,missense_variant,MODERATE,ENST00000000001,c.123A>G,p.Lys41Arg,Likely_pathogenic,Disease_A; Disease_B; not_provided,reviewed_by_expert_panel,rs0000001</example>\\nData background:\\nThe sample dataset is a simulated dataset for finding the genetic cause of Cystic fibrosis. The dataset is real sequencing data from CEPH_1463 dataset provided by the Complete Genomics Diversity Panel. It consists of sequencing of a family: 4 grandparents, 2 parents and 11 siblings. A known Mandelian disease mutation has been added on three siblings, taking care to be consistent with the underlying heplotype structure. The goal is to find the mutation causing the mendalian recessive trait - Cystic Fibrosis.\\n\\nConstraints:\\n1. Use only the benchmark inputs and references explicitly listed below.\\n2. Save the required final deliverables exactly to the paths listed below.\\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139\\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\\n5. Return a concise final summary after writing the required files.\\n\\nTask-specific instruction:\\nUse only the provided family variant data and ClinVar VCF. The final row should identify the causal CFTR recessive variant consistent with affected siblings.\\n\\nBenchmark data policy:\\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/data\\n- Allowed reference directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference\\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139\\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/results\\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than cystic-fibrosis>\\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\\n- Do not download external databases or install new packages during the benchmark run.\\n\\nInput data directory:\\n/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data\\nVisible input files:\\n- ex1.eff.vcf\\n- family_description.txt\\n\\nReference data directory:\\n/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference\\nVisible reference files:\\n- clinvar_20250521.vcf.gz\\n- clinvar_20250521.vcf.gz.tbi\\n\\nRequired final output paths:\\n- cf_variants.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv\", \"selected_resources_names\": {\"tools\": [{\"description\": \"Executes the provided Python command in the notebook environment and returns the output.\", \"name\": \"run_python_repl\", \"optional_parameters\": [], \"required_parameters\": [{\"default\": null, \"description\": \"Python command to execute in the notebook environment\", \"name\": \"command\", \"type\": \"str\"}], \"id\": 174, \"module\": \"biomni.tool.support_tools\"}, {\"description\": \"Convert a natural language prompt into a structured ClinVar search query and run it.\", \"name\": \"query_clinvar\", \"optional_parameters\": [{\"name\": \"search_term\", \"type\": \"str\", \"description\": \"Direct ClinVar search term\", \"default\": null}, {\"name\": \"max_results\", \"type\": \"int\", \"description\": \"Maximum number of results\", \"default\": 3}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about genetic variants\", \"default\": null}], \"id\": 189}, {\"description\": \"Query the NCBI dbSNP database using natural language or direct search term.\", \"name\": \"query_dbsnp\", \"optional_parameters\": [{\"name\": \"search_term\", \"type\": \"str\", \"description\": \"Direct dbSNP search term\", \"default\": null}, {\"name\": \"max_results\", \"type\": \"int\", \"description\": \"Maximum number of results\", \"default\": 3}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about SNPs/variants\", \"default\": null}], \"id\": 191}, {\"description\": \"Query the Ensembl REST API using natural language or a direct endpoint.\", \"name\": \"query_ensembl\", \"optional_parameters\": [{\"name\": \"endpoint\", \"type\": \"str\", \"description\": \"Direct Ensembl endpoint or full URL\", \"default\": null}, {\"name\": \"verbose\", \"type\": \"bool\", \"description\": \"Return detailed results\", \"default\": true}], \"required_parameters\": [{\"name\": \"prompt\", \"type\": \"str\", \"description\": \"Natural language query about genomic data\", \"default\": null}], \"id\": 193, \"module\": \"biomni.tool.database\"}, {\"name\": \"csvtk_headers\", \"description\": \"Print headers of a CSV/TSV file.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba09a0>\", \"id\": 237}, {\"name\": \"csvtk_dim\", \"description\": \"Dimensions of CSV file (rows and columns).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba1620>\", \"id\": 238}, {\"name\": \"csvtk_nrow\", \"description\": \"Print number of records (rows).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba28e0>\", \"id\": 240}, {\"name\": \"csvtk_summary\", \"description\": \"Summary statistics of selected numeric or text fields (groupby group fields).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"groups\": {\"type\": \"string\", \"description\": \"\", \"default\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"groups\", \"type\": \"string\", \"description\": \"\", \"default\": \"\"}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba19e0>\", \"id\": 242}, {\"name\": \"csvtk_cut\", \"description\": \"Select and arrange fields/columns.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba2480>\", \"id\": 243}, {\"name\": \"csvtk_grep\", \"description\": \"Grep data by selected fields with patterns/regular expressions.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"pattern\": {\"type\": \"string\", \"description\": \"\"}, \"ignore_case\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"invert_match\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"use_regexp\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"pattern\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"ignore_case\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"invert_match\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"use_regexp\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba0040>\", \"id\": 244}, {\"name\": \"csvtk_filter\", \"description\": \"Filter rows by values of selected fields with arithmetic expression (e.g., \\\"col1 > 10\\\").\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"filter_expr\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"filter_expr\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba1080>\", \"id\": 245}, {\"name\": \"csvtk_sort\", \"description\": \"Sort by selected fields. Keys format: 1:n (numeric), 2:r (reverse).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"keys\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"keys\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function 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\"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"left_join\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"outer_join\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba2200>\", \"id\": 248}, {\"name\": \"csvtk_concat\", \"description\": \"Concatenate CSV/TSV files by rows.\", \"parameters\": {\"files\": {\"type\": \"array\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"files\", \"type\": \"array\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba1580>\", \"id\": 249}, {\"name\": \"csvtk_uniq\", \"description\": \"Unique data without sorting.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba39c0>\", \"id\": 250}, {\"name\": \"csvtk_freq\", \"description\": \"Frequencies of selected fields.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"sort_by_freq\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"sort_by_freq\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba2660>\", \"id\": 251}, {\"name\": \"csvtk_mutate\", \"description\": \"Create new column from selected fields by regular expression.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"name\": {\"type\": \"string\", \"description\": \"\"}, \"pattern\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"name\", 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\"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"name\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"expression\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba1ee0>\", \"id\": 253}, {\"name\": \"csvtk_rename\", \"description\": \"Rename column names with new names.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"fields\": {\"type\": \"string\", \"description\": \"\"}, \"names\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"fields\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"names\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba2520>\", \"id\": 254}, {\"name\": \"csvtk_csv2md\", \"description\": \"Convert CSV to markdown format.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba1a80>\", \"id\": 262}, {\"name\": \"csvtk_head\", \"description\": \"Print first N records.\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"number\": {\"type\": \"number\", \"description\": \"\", \"default\": 10}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"no_header_row\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"-\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"number\", \"type\": \"number\", \"description\": \"\", \"default\": 10}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"no_header_row\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"-\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba1120>\", \"id\": 268}, {\"name\": \"csvtk_plot\", \"description\": \"Plot common figures (line, hist, box, etc.).\", \"parameters\": {\"in_file\": {\"type\": \"string\", \"description\": \"\"}, \"plot_type\": {\"type\": \"string\", \"description\": \"\"}, \"x_field\": {\"type\": \"string\", \"description\": \"\"}, \"y_field\": {\"type\": \"string\", \"description\": \"\", \"default\": \"\"}, \"title\": {\"type\": \"string\", \"description\": \"\", \"default\": \"\"}, \"tabs\": {\"type\": \"boolean\", \"description\": \"\", \"default\": false}, \"out_file\": {\"type\": \"string\", \"description\": \"\", \"default\": \"plot.png\"}}, \"required_parameters\": [{\"name\": \"in_file\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"plot_type\", \"type\": \"string\", \"description\": \"\", \"default\": null}, {\"name\": \"x_field\", \"type\": \"string\", \"description\": \"\", \"default\": null}], \"optional_parameters\": [{\"name\": \"y_field\", \"type\": \"string\", \"description\": \"\", \"default\": \"\"}, {\"name\": \"title\", \"type\": \"string\", \"description\": \"\", \"default\": \"\"}, {\"name\": \"tabs\", \"type\": \"boolean\", \"description\": \"\", \"default\": false}, {\"name\": \"out_file\", \"type\": \"string\", \"description\": \"\", \"default\": \"plot.png\"}], \"module\": \"mcp_servers.csvtk\", \"fn\": \"<function A1.add_mcp.<locals>.make_mcp_wrapper.<locals>.sync_tool_wrapper at 0x7f1da6ba22a0>\", \"id\": 275}], \"data_lake\": [], \"libraries\": [\"scvelo\", \"pandas\", \"numpy\", \"cyvcf2\", \"samtools\", \"bedtools\"], \"know_how\": []}}",
+  "planning_latency_seconds": 19.39006792753935,
+  "total_runtime_seconds": 118.70650419965386,
+  "selected_resources": {
+    "tools": [
+      {
+        "name": "run_python_repl",
+        "module": "biomni.tool.support_tools",
+        "description": "Executes the provided Python command in the notebook environment and returns the output."
+      },
+      {
+        "name": "query_clinvar",
+        "module": "biomni.tool.database",
+        "description": "Convert a natural language prompt into a structured ClinVar search query and run it."
+      },
+      {
+        "name": "query_dbsnp",
+        "module": "biomni.tool.database",
+        "description": "Query the NCBI dbSNP database using natural language or direct search term."
+      },
+      {
+        "name": "query_ensembl",
+        "module": "biomni.tool.database",
+        "description": "Query the Ensembl REST API using natural language or a direct endpoint."
+      },
+      {
+        "name": "csvtk_headers",
+        "module": "mcp_servers.csvtk",
+        "description": "Print headers of a CSV/TSV file."
+      },
+      {
+        "name": "csvtk_dim",
+        "module": "mcp_servers.csvtk",
+        "description": "Dimensions of CSV file (rows and columns)."
+      },
+      {
+        "name": "csvtk_nrow",
+        "module": "mcp_servers.csvtk",
+        "description": "Print number of records (rows)."
+      },
+      {
+        "name": "csvtk_summary",
+        "module": "mcp_servers.csvtk",
+        "description": "Summary statistics of selected numeric or text fields (groupby group fields)."
+      },
+      {
+        "name": "csvtk_cut",
+        "module": "mcp_servers.csvtk",
+        "description": "Select and arrange fields/columns."
+      },
+      {
+        "name": "csvtk_grep",
+        "module": "mcp_servers.csvtk",
+        "description": "Grep data by selected fields with patterns/regular expressions."
+      },
+      {
+        "name": "csvtk_filter",
+        "module": "mcp_servers.csvtk",
+        "description": "Filter rows by values of selected fields with arithmetic expression (e.g., \"col1 > 10\")."
+      },
+      {
+        "name": "csvtk_sort",
+        "module": "mcp_servers.csvtk",
+        "description": "Sort by selected fields. Keys format: 1:n (numeric), 2:r (reverse)."
+      },
+      {
+        "name": "csvtk_join",
+        "module": "mcp_servers.csvtk",
+        "description": "Join files by selected fields."
+      },
+      {
+        "name": "csvtk_concat",
+        "module": "mcp_servers.csvtk",
+        "description": "Concatenate CSV/TSV files by rows."
+      },
+      {
+        "name": "csvtk_uniq",
+        "module": "mcp_servers.csvtk",
+        "description": "Unique data without sorting."
+      },
+      {
+        "name": "csvtk_freq",
+        "module": "mcp_servers.csvtk",
+        "description": "Frequencies of selected fields."
+      },
+      {
+        "name": "csvtk_mutate",
+        "module": "mcp_servers.csvtk",
+        "description": "Create new column from selected fields by regular expression."
+      },
+      {
+        "name": "csvtk_mutate2",
+        "module": "mcp_servers.csvtk",
+        "description": "Create a new column from selected fields by awk-like arithmetic/string expressions."
+      },
+      {
+        "name": "csvtk_rename",
+        "module": "mcp_servers.csvtk",
+        "description": "Rename column names with new names."
+      },
+      {
+        "name": "csvtk_csv2md",
+        "module": "mcp_servers.csvtk",
+        "description": "Convert CSV to markdown format."
+      },
+      {
+        "name": "csvtk_head",
+        "module": "mcp_servers.csvtk",
+        "description": "Print first N records."
+      },
+      {
+        "name": "csvtk_plot",
+        "module": "mcp_servers.csvtk",
+        "description": "Plot common figures (line, hist, box, etc.)."
+      }
+    ],
+    "data_lake": [],
+    "libraries": [
+      {
+        "name": "scvelo",
+        "description": "[Python Package] A tool for RNA velocity analysis in single cells using dynamical models."
+      },
+      {
+        "name": "pandas",
+        "description": "[Python Package] A fast, powerful, and flexible data analysis and manipulation library for Python."
+      },
+      {
+        "name": "numpy",
+        "description": "[Python Package] The fundamental package for scientific computing with Python, providing support for arrays, matrices, and mathematical functions."
+      },
+      {
+        "name": "cyvcf2",
+        "description": "[Python Package] Fast parsing of VCF files."
+      },
+      {
+        "name": "samtools",
+        "description": "[CLI Tool] A suite of programs for interacting with high-throughput sequencing data. Use with subprocess.run(['samtools', ...])."
+      },
+      {
+        "name": "bedtools",
+        "description": "[CLI Tool] A powerful toolset for genome arithmetic, allowing operations like intersect, merge, count, and complement on genomic features. Use with subprocess.run(['bedtools', ...])."
+      }
+    ],
+    "know_how": []
+  },
+  "selected_resource_names": {
+    "tools": [
+      "run_python_repl",
+      "query_clinvar",
+      "query_dbsnp",
+      "query_ensembl",
+      "csvtk_headers",
+      "csvtk_dim",
+      "csvtk_nrow",
+      "csvtk_summary",
+      "csvtk_cut",
+      "csvtk_grep",
+      "csvtk_filter",
+      "csvtk_sort",
+      "csvtk_join",
+      "csvtk_concat",
+      "csvtk_uniq",
+      "csvtk_freq",
+      "csvtk_mutate",
+      "csvtk_mutate2",
+      "csvtk_rename",
+      "csvtk_csv2md",
+      "csvtk_head",
+      "csvtk_plot"
+    ],
+    "data_lake": [],
+    "libraries": [
+      "scvelo",
+      "pandas",
+      "numpy",
+      "cyvcf2",
+      "samtools",
+      "bedtools"
+    ],
+    "know_how": []
+  },
+  "registered_tool_count": 331,
+  "registered_tool_names": [
+    "fetch_supplementary_info_from_doi",
+    "query_arxiv",
+    "query_scholar",
+    "query_pubmed",
+    "search_google",
+    "extract_url_content",
+    "extract_pdf_content",
+    "advanced_web_search_claude",
+    "analyze_circular_dichroism_spectra",
+    "analyze_rna_secondary_structure_features",
+    "analyze_protease_kinetics",
+    "analyze_enzyme_kinetics_assay",
+    "analyze_itc_binding_thermodynamics",
+    "analyze_protein_conservation",
+    "split_modalities",
+    "prepare_input_for_nnunet",
+    "segment_with_nn_unet",
+    "create_segmentation_visualization",
+    "quick_rigid_registration",
+    "quick_affine_registration",
+    "quick_deformable_registration",
+    "batch_register_images",
+    "calculate_similarity_metrics",
+    "create_registration_visualization",
+    "analyze_cell_migration_metrics",
+    "perform_crispr_cas9_genome_editing",
+    "analyze_calcium_imaging_data",
+    "analyze_in_vitro_drug_release_kinetics",
+    "analyze_myofiber_morphology",
+    "decode_behavior_from_neural_trajectories",
+    "simulate_whole_cell_ode_model",
+    "predict_protein_disorder_regions",
+    "analyze_cell_morphology_and_cytoskeleton",
+    "analyze_tissue_deformation_flow",
+    "find_n_glycosylation_motifs",
+    "predict_o_glycosylation_hotspots",
+    "list_glycoengineering_resources",
+    "analyze_ddr_network_in_cancer",
+    "analyze_cell_senescence_and_apoptosis",
+    "detect_and_annotate_somatic_mutations",
+    "detect_and_characterize_structural_variations",
+    "perform_gene_expression_nmf_analysis",
+    "analyze_copy_number_purity_ploidy_and_focal_events",
+    "quantify_cell_cycle_phases_from_microscopy",
+    "quantify_and_cluster_cell_motility",
+    "perform_facs_cell_sorting",
+    "analyze_flow_cytometry_immunophenotyping",
+    "analyze_mitochondrial_morphology_and_potential",
+    "annotate_open_reading_frames",
+    "annotate_plasmid",
+    "get_gene_coding_sequence",
+    "get_plasmid_sequence",
+    "align_sequences",
+    "pcr_simple",
+    "digest_sequence",
+    "find_restriction_sites",
+    "find_restriction_enzymes",
+    "find_sequence_mutations",
+    "design_knockout_sgrna",
+    "get_oligo_annealing_protocol",
+    "get_golden_gate_assembly_protocol",
+    "get_bacterial_transformation_protocol",
+    "design_primer",
+    "design_verification_primers",
+    "design_golden_gate_oligos",
+    "golden_gate_assembly",
+    "liftover_coordinates",
+    "bayesian_finemapping_with_deep_vi",
+    "analyze_cas9_mutation_outcomes",
+    "analyze_crispr_genome_editing",
+    "simulate_demographic_history",
+    "identify_transcription_factor_binding_sites",
+    "fit_genomic_prediction_model",
+    "perform_pcr_and_gel_electrophoresis",
+    "analyze_protein_phylogeny",
+    "annotate_celltype_scRNA",
+    "annotate_celltype_with_panhumanpy",
+    "create_scvi_embeddings_scRNA",
+    "create_harmony_embeddings_scRNA",
+    "get_uce_embeddings_scRNA",
+    "map_to_ima_interpret_scRNA",
+    "get_rna_seq_archs4",
+    "get_gene_set_enrichment_analysis_supported_database_list",
+    "gene_set_enrichment_analysis",
+    "analyze_chromatin_interactions",
+    "analyze_comparative_genomics_and_haplotypes",
+    "perform_chipseq_peak_calling_with_macs2",
+    "find_enriched_motifs_with_homer",
+    "analyze_genomic_region_overlap",
+    "unsupervised_celltype_transfer_between_scRNA_datasets",
+    "generate_embeddings_with_state",
+    "interspecies_gene_conversion",
+    "generate_gene_embeddings_with_ESM_models",
+    "generate_transcriptformer_embeddings",
+    "analyze_atac_seq_differential_accessibility",
+    "analyze_bacterial_growth_curve",
+    "isolate_purify_immune_cells",
+    "estimate_cell_cycle_phase_durations",
+    "track_immune_cells_under_flow",
+    "analyze_cfse_cell_proliferation",
+    "analyze_cytokine_production_in_cd4_tcells",
+    "analyze_ebv_antibody_titers",
+    "analyze_cns_lesion_histology",
+    "analyze_immunohistochemistry_image",
+    "optimize_anaerobic_digestion_process",
+    "analyze_arsenic_speciation_hplc_icpms",
+    "count_bacterial_colonies",
+    "annotate_bacterial_genome",
+    "enumerate_bacterial_cfu_by_serial_dilution",
+    "model_bacterial_growth_dynamics",
+    "quantify_biofilm_biomass_crystal_violet",
+    "segment_and_analyze_microbial_cells",
+    "segment_cells_with_deep_learning",
+    "simulate_generalized_lotka_volterra_dynamics",
+    "predict_rna_secondary_structure",
+    "simulate_microbial_population_dynamics",
+    "analyze_aortic_diameter_and_geometry",
+    "analyze_atp_luminescence_assay",
+    "analyze_thrombus_histology",
+    "analyze_intracellular_calcium_with_rhod2",
+    "quantify_corneal_nerve_fibers",
+    "segment_and_quantify_cells_in_multiplexed_images",
+    "analyze_bone_microct_morphometry",
+    "run_diffdock_with_smiles",
+    "docking_autodock_vina",
+    "run_autosite",
+    "retrieve_topk_repurposing_drugs_from_disease_txgnn",
+    "predict_admet_properties",
+    "predict_binding_affinity_protein_1d_sequence",
+    "analyze_accelerated_stability_of_pharmaceutical_formulations",
+    "run_3d_chondrogenic_aggregate_assay",
+    "grade_adverse_events_using_vcog_ctcae",
+    "analyze_radiolabeled_antibody_biodistribution",
+    "estimate_alpha_particle_radiotherapy_dosimetry",
+    "perform_mwas_cyp2c19_metabolizer_status",
+    "calculate_physicochemical_properties",
+    "analyze_xenograft_tumor_growth_inhibition",
+    "analyze_pixel_distribution",
+    "find_roi_from_image",
+    "analyze_western_blot",
+    "query_drug_interactions",
+    "check_drug_combination_safety",
+    "analyze_interaction_mechanisms",
+    "find_alternative_drugs_ddinter",
+    "query_fda_adverse_events",
+    "get_fda_drug_label_info",
+    "check_fda_drug_recalls",
+    "analyze_fda_safety_signals",
+    "reconstruct_3d_face_from_mri",
+    "analyze_abr_waveform_p1_metrics",
+    "analyze_ciliary_beat_frequency",
+    "analyze_protein_colocalization",
+    "perform_cosinor_analysis",
+    "calculate_brain_adc_map",
+    "analyze_endolysosomal_calcium_dynamics",
+    "analyze_fatty_acid_composition_by_gc",
+    "analyze_hemodynamic_data",
+    "simulate_thyroid_hormone_pharmacokinetics",
+    "quantify_amyloid_beta_plaques",
+    "engineer_bacterial_genome_for_therapeutic_delivery",
+    "analyze_bacterial_growth_rate",
+    "analyze_barcode_sequencing_data",
+    "analyze_bifurcation_diagram",
+    "create_biochemical_network_sbml_model",
+    "optimize_codons_for_heterologous_expression",
+    "simulate_gene_circuit_with_growth_feedback",
+    "identify_fas_functional_domains",
+    "perform_flux_balance_analysis",
+    "model_protein_dimerization_network",
+    "simulate_metabolic_network_perturbation",
+    "simulate_protein_signaling_network",
+    "compare_protein_structures",
+    "simulate_renin_angiotensin_system_dynamics",
+    "query_chatnt",
+    "run_python_repl",
+    "read_function_source_code",
+    "download_synapse_data",
+    "query_uniprot",
+    "query_alphafold",
+    "query_interpro",
+    "query_pdb",
+    "query_pdb_identifiers",
+    "query_kegg",
+    "query_stringdb",
+    "query_iucn",
+    "query_paleobiology",
+    "query_jaspar",
+    "query_worms",
+    "query_cbioportal",
+    "query_clinvar",
+    "query_geo",
+    "query_dbsnp",
+    "query_ucsc",
+    "query_ensembl",
+    "query_opentarget",
+    "query_monarch",
+    "query_openfda",
+    "query_gwas_catalog",
+    "query_gnomad",
+    "blast_sequence",
+    "query_reactome",
+    "query_regulomedb",
+    "query_pride",
+    "query_gtopdb",
+    "query_remap",
+    "query_mpd",
+    "query_emdb",
+    "query_synapse",
+    "query_pubchem",
+    "query_chembl",
+    "query_unichem",
+    "query_clinicaltrials",
+    "query_dailymed",
+    "query_quickgo",
+    "query_encode",
+    "region_to_ccre_screen",
+    "get_genes_near_ccre",
+    "test_pylabrobot_script",
+    "get_pylabrobot_documentation_liquid",
+    "get_pylabrobot_documentation_material",
+    "search_protocols",
+    "get_protocol_details",
+    "list_local_protocols",
+    "read_local_protocol",
+    "kallisto_index",
+    "kallisto_quant",
+    "kallisto_bus",
+    "kallisto_quant_tcc",
+    "kallisto_h5dump",
+    "kallisto_inspect",
+    "kallisto_version",
+    "kallisto_cite",
+    "kallisto_bus_list_technologies",
+    "kallisto_merge",
+    "kraken2_classify",
+    "kraken2_build_db",
+    "kraken2_inspect_db",
+    "csvtk_headers",
+    "csvtk_dim",
+    "csvtk_ncol",
+    "csvtk_nrow",
+    "csvtk_corr",
+    "csvtk_summary",
+    "csvtk_cut",
+    "csvtk_grep",
+    "csvtk_filter",
+    "csvtk_filter2",
+    "csvtk_sort",
+    "csvtk_join",
+    "csvtk_concat",
+    "csvtk_uniq",
+    "csvtk_freq",
+    "csvtk_mutate",
+    "csvtk_mutate2",
+    "csvtk_rename",
+    "csvtk_replace",
+    "csvtk_round",
+    "csvtk_transpose",
+    "csvtk_sep",
+    "csvtk_gather",
+    "csvtk_spread",
+    "csvtk_pretty",
+    "csvtk_csv2md",
+    "csvtk_csv2json",
+    "csvtk_xlsx2csv",
+    "csvtk_fix",
+    "csvtk_fix_quotes",
+    "csvtk_del_quotes",
+    "csvtk_head",
+    "csvtk_sample",
+    "csvtk_split",
+    "csvtk_comb",
+    "csvtk_fmtdate",
+    "csvtk_fold",
+    "csvtk_unfold",
+    "csvtk_plot",
+    "csvtk_version",
+    "megahit_assemble",
+    "megahit_core_contig2fastg",
+    "kaiju_classify",
+    "kaiju_makedb",
+    "kaiju_mkbwt",
+    "kaiju_mkfmi",
+    "kaiju_multi_classify",
+    "kaiju2krona",
+    "kaiju2table",
+    "kaiju_add_taxon_names",
+    "kaiju_merge_outputs",
+    "kaijux_search",
+    "kaijup_search",
+    "fastp_tool",
+    "spades_py",
+    "metaspades_py",
+    "rnaspades_py",
+    "plasmidspades_py",
+    "metaviralspades_py",
+    "coronaspades_py",
+    "biosyntheticspades_py",
+    "spades_test",
+    "spades_kmercount",
+    "spades_hammer",
+    "settings",
+    "scanpy_filter",
+    "scanpy_norm",
+    "scanpy_log1p",
+    "scanpy_hvg",
+    "scanpy_scale",
+    "scanpy_pca",
+    "scanpy_neighbors",
+    "scanpy_umap",
+    "scanpy_tsne",
+    "scanpy_diffexp",
+    "scanpy_louvain",
+    "scanpy_leiden",
+    "scanpy_paga",
+    "scanpy_cli_read",
+    "scanpy_cli_filter",
+    "scanpy_cli_norm",
+    "scanpy_cli_hvg",
+    "scanpy_cli_scale",
+    "scanpy_cli_regress",
+    "scanpy_cli_pca",
+    "scanpy_cli_neighbor",
+    "scanpy_cli_embed",
+    "scanpy_cli_cluster",
+    "scanpy_cli_diffexp",
+    "scanpy_cli_paga",
+    "scanpy_cli_dpt",
+    "scanpy_cli_integrate",
+    "scanpy_cli_multiplet",
+    "scanpy_cli_plot"
+  ]
+}

Biomni/experiments/bioagent_bench/runs/scale_100/cystic-fibrosis_20260514_192139/run_metadata.json

@@ -0,0 +1,32 @@
+{
+  "task_id": "cystic-fibrosis",
+  "task_name": "Cystic Fibrosis Mendelian Variant Identification",
+  "run_dir": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139",
+  "dataset_dir": "/225040511/project/bioagent-bench/dataset/cystic-fibrosis",
+  "data_dir": "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data",
+  "reference_dir": "/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference",
+  "agent_runtime_dir": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/agent_runtime",
+  "output_paths": [
+    "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv"
+  ],
+  "mcp_config": "/225040511/project/Biomni/experiments/mcp_tool_scaling/configs/mcp_scale_100.yaml",
+  "agent_kwargs": {
+    "path": "/225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/agent_runtime",
+    "expected_data_lake_files": [],
+    "use_tool_retriever": true,
+    "timeout_seconds": 1200,
+    "llm": "deepseek-v4-flash",
+    "source": "Custom",
+    "base_url": "https://api.deepseek.com/v1",
+    "api_key": "sk-06e6154722b84e89b081b1c9571838ef"
+  },
+  "query": "You are running a bioagent-bench task with local files already prepared.\n\nTask ID: cystic-fibrosis\nTask name: Cystic Fibrosis Mendelian Variant Identification\nBenchmark prompt:\nFind the genetic cause of Cystic fibrosis; identify the causal recessive variant consistent with affected siblings NA12885, NA12886, and NA12879. The output should be a CSV file with the following columns: chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id. <example>chromosome,position,variant_id,reference,alternate,gene_name,gene_id,annotation,impact,transcript_id,hgvs_c,hgvs_p,clinical_significance,diseases,review_status,rs_id\nX,123456789,VAR123,A,G,GENE1,ENSG00000000001,missense_variant,MODERATE,ENST00000000001,c.123A>G,p.Lys41Arg,Likely_pathogenic,Disease_A; Disease_B; not_provided,reviewed_by_expert_panel,rs0000001</example>\nData background:\nThe sample dataset is a simulated dataset for finding the genetic cause of Cystic fibrosis. The dataset is real sequencing data from CEPH_1463 dataset provided by the Complete Genomics Diversity Panel. It consists of sequencing of a family: 4 grandparents, 2 parents and 11 siblings. A known Mandelian disease mutation has been added on three siblings, taking care to be consistent with the underlying heplotype structure. The goal is to find the mutation causing the mendalian recessive trait - Cystic Fibrosis.\n\nConstraints:\n1. Use only the benchmark inputs and references explicitly listed below.\n2. Save the required final deliverables exactly to the paths listed below.\n3. Save all intermediate scripts, logs, and scratch outputs inside this run directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139\n4. Keep final deliverables in the same schema/format requested by the benchmark prompt.\n5. Return a concise final summary after writing the required files.\n\nTask-specific instruction:\nUse only the provided family variant data and ClinVar VCF. The final row should identify the causal CFTR recessive variant consistent with affected siblings.\n\nBenchmark data policy:\n- Allowed input data directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/data\n- Allowed reference directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference\n- Allowed scratch/output directory: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139\n- Forbidden truth/results directory: /225040511/project/bioagent-bench/dataset/cystic-fibrosis/results\n- Forbidden sibling benchmark task directories: /225040511/project/bioagent-bench/dataset/<any task other than cystic-fibrosis>\n- Do not inspect previous bioagent-bench-runs or sibling task outputs as data sources.\n- Do not download external databases or install new packages during the benchmark run.\n\nInput data directory:\n/225040511/project/bioagent-bench/dataset/cystic-fibrosis/data\nVisible input files:\n- ex1.eff.vcf\n- family_description.txt\n\nReference data directory:\n/225040511/project/bioagent-bench/dataset/cystic-fibrosis/reference\nVisible reference files:\n- clinvar_20250521.vcf.gz\n- clinvar_20250521.vcf.gz.tbi\n\nRequired final output paths:\n- cf_variants.csv: /225040511/project/Biomni/experiments/mcp_tool_scaling/runs/scale_100/cystic-fibrosis_20260514_192139/cf_variants.csv",
+  "timestamp_utc": "20260514_192139",
+  "runtime_environment": {
+    "execution_env_prefix": "/225040511/miniconda3/envs/biomni_e1",
+    "execution_python": "/225040511/miniconda3/envs/biomni_e1/bin/python",
+    "conda_default_env": "biomni_e1",
+    "conda_prefix": "/225040511/miniconda3/envs/biomni_e1"
+  },
+  "biomni_root": "/225040511/project/Biomni"
+}